# templates

name: Export Search Console Results to Google Sheets
author_name: Zacharia Kimotho
author_username: imperolq
categories: []
created_at: 2024-12-07T05:11:48.246Z
updated_at: 2024-12-07T05:11:48.246Z
url: https://n8n.io/workflows/2613
scraped_at: 2025-09-14 11:38:22
description:
  How it works

  This workflow gets the search console results data and exports this to google sheets. This makes it easier to visualize and do other SEO related tasks and activities without having to log into Search Console

  Setup and use

  Set your desired schedule
  Enter your desired domain
  Connect to your Google sheets or make a copy of this sheet. 

  Detailed Setup

  Inputs and Outputs:**
  Input: API response from Google Search Console regarding keywords, page data, and date data.
  Output: Entries written to Google Sheets containing keyword data, clicks, impressions, CTR, and positions.

  Setup Instructions:
  Prerequisites:**
  An n8n instance set up and running.
  Active Google Account with access to Google Search Console and Google Sheets.
  Google OAuth 2.0 credentials for API access.

  Step-by-Step Setup:**
  Open n8n and create a new workflow.
  Add the nodes as described in the JSON.
  Configure the Google OAuth2 credentials in n8n to enable API access.
  Set your domain in the Set your domain node.
  Customize the Google Sheets document URLs to your personal sheets.
  Adjust the schedule in the Schedule Trigger node as per your requirements.
  Save the workflow.

  Configuration Options:**
  You can customize the date ranges in the body of the HttpRequest nodes.
  Adjust any fields in the Edit Fields nodes based on different data requirements.

  Use Case Examples:
  Useful in tracking website performance over time using Search Console metrics.
  Ideal for digital marketers, SEO specialists, and web analytics professionals.
  Offers value in compiling performance reports for stakeholders or team reviews.

  Running and Troubleshooting:
  Running the Workflow:**
  Trigger the workflow manually or wait for the schedule to run it automatically.

  Monitoring Execution:**
  Check the execution logs in n8n's dashboard to ensure all nodes complete successfully.

  Common Issues:**
  Invalid OAuth credentials – ensure credentials are set up correctly.
  Incorrect Google Sheets URLs – double-check document links and permissions.
  Scheduling conflicts – make sure the schedule set does not overlap with other workflows.

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

workflow_json_compressed:
  [encoded data, 4904 characters]


name: Extract text from PDF and image using Vertex AI (Gemini) into CSV
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2024-12-08T13:07:12.471Z
updated_at: 2024-12-08T13:07:12.471Z
url: https://n8n.io/workflows/2614
scraped_at: 2025-09-14 11:38:21
description:
  Case Study

  I'm too lazy to record every transaction for my expense tracking. Since all my expenses are digital, I just extract the transactions from bank PDF statements and screenshots into CSV to import into my budgeting software.

  Click here to watch Youtube tutorial

  What this workflow does

  Upload your PDF or screenshots into Google Drive 
  It then passes the PDF/image to Vertex Gemini to do some A.I. image recognition
  It then sends the transactions as CSV and stores it into another Google Drive folder

  Setup
  Set up 2 google drive folders. 1 for uploading and 1 for the output.
  Input your Google Drive crendtials
  Input your Vertex Gemini credentials

  How to adjust it to your needs
  You can upload other types of documents for information extraction. 
  You can extract any text data from any image or PDF
  You can adjust the A.I. prompt to do different things

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.switch","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.googleDrive","n8n-nodes-base.convertToFile","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm"]

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name: Get Airtable data via AI and Obsidian Notes
author_name: Obsidi8n
author_username: derruttie
categories: []
created_at: 2024-12-08T19:21:21.748Z
updated_at: 2024-12-08T19:21:21.748Z
url: https://n8n.io/workflows/2615
scraped_at: 2025-09-14 11:38:24
description:
  I am submitting this workflow for the Obsidian community to showcase the potential of integrating Obsidian with n8n. While straightforward, it serves as a compelling demonstration of the potential unlocked by integrating Obsidian with n8n.

  How it works

  This workflow  lets you retrieve specific Airtable data you need in seconds, directly within your Obsidian note, using n8n. By highlighting a question in Obsidian and sending it to a webhook via the Post Webhook Plugin, you can fetch specific data from your Airtable base and instantly insert the response back into your note. The workflow leverages OpenAI’s GPT model to interpret your query, extract relevant data from Airtable, and format the result for seamless integration into your note.

  Set up steps

  Install the Post Webhook Plugin: Add this plugin to your Obsidian vault from the plugin store or GitHub.
  Set up the n8n Webhook: Copy the webhook URL generated in this workflow and insert it into the Post Webhook Plugin's settings in Obsidian.
  Configure Airtable Access: Link your Airtable account and specify the desired base and table to pull data from.
  Test the Workflow: Highlight a question in your Obsidian note, use the “Send Selection to Webhook” command, and verify that data is returned as expected.

nodes_used:
  ["n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook"]

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name: Email Subscription Service with n8n Forms, Airtable and AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-10T10:42:16.009Z
updated_at: 2024-12-10T10:42:16.009Z
url: https://n8n.io/workflows/2618
scraped_at: 2025-09-14 11:38:23
description:
  This n8n template shows how anyone can build a simple newsletter-like subscription service where users can enrol themselves to receive messages/content on a regular basis. It uses n8n forms for data capture, Airtable for database, AI for content generation and Gmail for email sending.

  How it works
  An n8n form is setup up to allow users to subscribe with a desired topic and interval of which to recieve messages via n8n forms which is then added to the Airtable.
  A scheduled trigger is executed every morning and searches for subscribers to send messages for based on their desired intervals.
  Once found, Subscribers are sent to a subworkflow which performs the text content generation via an AI agent and also uses a vision model to generate an image.
  Both are attached to an email which is sent to the subscriber. This email also includes an unsubscribe link.
  The unsubscribe flow works similarly via n8n form interface which when submitted disables further scheduled emails to the user.

  How to use
  Make a copy of sample Airtable here: https://airtable.com/appL3dptT6ZTSzY9v/shrLukHafy5bwDRfD
  Make sure the workflow is "activated" and the forms are available and reachable by your audience.

  Requirements
  Airtable for Database
  OpenAI for LLM (but compatible with others)
  Gmail for Email (but can be replaced with others)

  Customising this workflow
  This simple use can be extended to deliver any types of content such as your company newsletter, promotions, social media posts etc.
  Doesn't have to be limited to just email - try social messaging, Whatsapp, Telegram and others.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.gmail","n8n-nodes-base.executeWorkflow","n8n-nodes-base.set","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.formTrigger","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.filter","n8n-nodes-base.code","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.executionData","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWikipedia","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGroq","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.editImage","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Deduplicate Scraping AI Grants for Eligibility using AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-10T11:00:25.179Z
updated_at: 2024-12-10T11:00:25.179Z
url: https://n8n.io/workflows/2619
scraped_at: 2025-09-14 11:38:23
description:
  This n8n template scrapes a list of AI grants from grants.gov and qualifies them using AI; determining interest and eligibility for the business. It then sends an email alert of interesting items to team members in an email.

  The template also shows how you can use the "Remove Duplicates" node to simplify deduplication of external listings without the need to manage this yourself.

  Not particularly interested in AI Grants? This template works for other tender websites as long as you're able to scrape them.

  How it works
  A scheduled trigger is set to fetch a list of AI grants listed on the grants.gov website in the past day.
  A Remove Duplicates node is used to track Grant IDs to filter out those already processed by the workflow.
  New grants are summarized and analysed by AI nodes to determine eligibility and interest which is then saved to an Airtable database.
  Another scheduled trigger starts a little later  than the first to collect and summarize the new grants
  The results are then compiled into an email template using the HTML node, in the form of a newsletter designed to alert and brief team members of new AI grants.
  This email is then sent to a list of subscribers using the gmail node.

  How to use
  Make a copy of sample Airtable here: https://airtable.com/appiNoPRvhJxz9crl/shrRdP6zstgsxjDKL
  The filters for fetching the grants is currently set to the "AI" category. Feel free to change this to include more categories.
  Not interested in grants, this template can works for other sources of leads just change the endpoint and how you're defining the item ID to track.

  Requirements
  Airtable for database
  OpenAI for LLM

  Note: These are not hard requirements and can be exchanged for services available to you.

  customising the workflow
  "Eligibility" criteria at this stage may be better served by identifying hard blockers instead ie. certifications, geographical considerations or certain legal checks. Be sure to mention any hard blockers into the Eligibility prompt.
  Not particularly interested in AI prompts? This template works for other tender websites as long as you're able to scrape them.

nodes_used:
  ["n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.merge","n8n-nodes-base.airtable","n8n-nodes-base.removeDuplicates","n8n-nodes-base.httpRequest","n8n-nodes-base.airtable","n8n-nodes-base.html","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.airtable","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 11772 characters]


name: Calendly to KlickTipp Integration
author_name: KlickTipp
author_username: KlickTipp
categories: []
created_at: 2024-12-10T15:13:32.783Z
updated_at: 2024-12-10T15:13:32.783Z
url: https://n8n.io/workflows/2620
scraped_at: 2025-09-14 11:38:24
description:
  Community Node Disclaimer: This workflow uses KlickTipp community nodes.

  How It Works  

  Enhanced Calendly Integration: This workflow processes bookings and cancellations in Calendly, dynamically managing invitee and guest data with KlickTipp.  

  Data Transformation: Dates and times are converted into formats (UNIX timestamps) compatible with KlickTipp’s API, ensuring seamless data integration.  

  Key Features  

  Calendly Trigger:  
  Captures new bookings or cancellations of events, including participant details.  

  Invitee and Guest Subscription in KlickTipp:  
  Adds or updates invitees and guests in KlickTipp based on booking details (event name, time, join link, reschedule link, cancel link, etc.).  
  Tracks and processes cancellations for both invitees and guests.  
  Handles rescheduling intelligently to avoid redundant operations.  

  Guest-Specific Operations:  
  Processes guests individually for bookings and cancellations using dynamic arrays of email addresses.  
  Recovers guest data from invitee records for cancellations since Calendly does not provide guest data upon cancellation.  

  Data Processing:  
  Standardizes and validates input fields  
  Converts phone numbers to numeric-only format with international prefixes.  
  Transforms dates into UNIX timestamps.  
  Reads out the name of the invitee based on both possible input fields for name (name vs. firstname and lastname field setup).  

  Error Handling:  
  Validates critical fields like phone numbers, URLs, and dates to prevent incorrect data submissions.  

  Setup Instructions  

  Authentication:  
  Set up the Calendly and KlickTipp nodes in your n8n instance.  
  Configure authentication for both Calendly and KlickTipp nodes.  

  Custom Field Preparation in KlickTipp:  
  Create the following custom fields in KlickTipp to align with workflow requirements:  

  | Field Name                        | Field Type   |
  |---------------------------------------|------------------|
  | Calendly \| event name                | Text             |
  | Calendly \| join url                  | URL              |
  | Calendly \| reschedule url            | URL              |
  | Calendly \| cancel url                | URL              |
  | Calendly \| event start datetime      | Date & Time      |
  | Calendly \| event end datetime        | Date & Time      |
  | Calendly \| invitee start date        | Date             |
  | Calendly \| invitee end date          | Date             |
  | Calendly \| invitee start time        | Time             |
  | Calendly \| invitee end time          | Time             |
  | Calendly \| invitee timezone          | Text             |
  | Calendly \| invitee guests addresses  | Text             |


  After creating fields, allow 10-15 minutes for them to sync. If fields don’t appear, reconnect your KlickTipp credentials.  

  Field Mapping and Adjustments:  
  Open each KlickTipp node and map fields to match your setup. The workflow includes placeholders for:  
  Invitee details (first name, last name, email, and phone).  
  Event details (start/end times, timezone, etc.).  

  Workflow Logic  

  Trigger via Calendly event Booking:  
     A new form event booking or cancellation from Calendly initiates the workflow  

  Data Transformation:  
     Processes raw Calendly event data to ensure compatibility with KlickTipp’s API.  

  Add to KlickTipp Subscriber List:  
     Adds invitees and guests to the designated KlickTipp list, including event-specific details.  

  Benefits  

  Efficient lead generation: Contacts from event bookings are automatically imported into KlickTipp and can be used immediately, saving time and increasing the conversion rate.  
  Automated processes: Experts can start workflows directly, such as reminder emails or course admissions, reducing administrative effort.  
  Error-free data management: The template ensures precise data mapping, avoids manual corrections and reinforces a professional appearance.  

  Testing and Deployment  

  Test the workflow by triggering a Calendly event and verifying data updates in KlickTipp.  

  Notes:  

  Customization:  
  Update field mappings within the KlickTipp nodes to align with your account setup. This ensures accurate data syncing.  

  Resources:
  Calendly KlickTipp Knowledge Base help article
  Use KlickTipp Community Node in n8n
  Automate Workflows: KlickTipp Integration in n8n

nodes_used:
  ["n8n-nodes-klicktipp.klicktipp","n8n-nodes-klicktipp.klicktipp","n8n-nodes-klicktipp.klicktipp","n8n-nodes-klicktipp.klicktipp","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-base.calendlyTrigger","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.noOp","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, 7040 characters]


name: AI Agent To Chat With Files In Supabase Storage
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-10T19:04:24.160Z
updated_at: 2024-12-10T19:04:24.160Z
url: https://n8n.io/workflows/2621
scraped_at: 2025-09-14 11:38:20
description:
  Video Guide

  I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n.



  Youtube Link

  Who is this for?
  This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive.

  What problem does this workflow solve?
  Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files.

  What this workflow does
  The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include:
  Fetching and comparing files to avoid duplicate processing.
  Handling file downloads and extracting content based on the file type.
  Converting documents into vectorized data for contextual information retrieval.
  Storing and querying vectorized data from a Supabase vector store.

  File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content.
  Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions.
  Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot.

  Setup

  N8N Workflow
  Fetch File List from Supabase:
     Use Supabase to retrieve the stored file list from a specified bucket.
     Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing.

  Compare and Filter Files:
     Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table.
     Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled.

  Handle File Downloads:
     Download new files using detailed storage configurations for public/private access.
     Adjust the storage settings and GET requests to match your Supabase setup.

  File Type Processing:
     Use a Switch node to target specific file types (e.g., PDFs or text files).
     Employ relevant tools to process the content:
       For PDFs, extract embedded content.
       For text files, directly process the text data.

  Content Chunking:
     Break large text data into smaller chunks using the Text Splitter node.
     Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks.

  Vector Embedding Creation:
     Generate vectorized embeddings for the processed content using OpenAI's embedding tools.
     Ensure metadata, such as file ID, is included for easy data retrieval.

  Store Vectorized Data:
     Save the vectorized information into a dedicated Supabase vector store.
     Use the default schema and table provided by Supabase for seamless setup.

  AI Chatbot Integration:
     Add a chatbot node to handle user input and retrieve relevant document chunks.
     Use metadata like file ID for targeted queries, especially when multiple documents are involved.

  Testing
  Upload sample files to your Supabase bucket.
  Verify if files are processed and stored successfully in the vector store.
  Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?").
  Test for accuracy and contextual relevance of retrieved results.

nodes_used:
  ["n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.supabase","n8n-nodes-base.if","n8n-nodes-base.supabase","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.manualTrigger","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.agent","@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.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, 6544 characters]


name: Generate Amazon FBA keywords using Amazon Completion API
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2024-12-11T02:15:20.745Z
updated_at: 2024-12-11T02:15:20.745Z
url: https://n8n.io/workflows/2629
scraped_at: 2025-09-14 11:38:23
description:
  Use Case
  Generate keywords for your Amazon product.

  You want to find more keywords for advertising your product on Amazon.

  Click here to watch youtube tutorial

  What this Workflow Does 

  The workflow connects to Amazon's completion API to retrieve keywords for your advertising campaigns



  Use Airtable to send the workflow your keywords. Download airtable here.
  It sends the keyword to Amazon and returns keywords related to your keyword.
  It stores the results back into Airtable.

  How to Adjust it to Your Needs

  You can modify the Amazon Completion API URL to get more data back from Amazon.

  Made by Rumjahn

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

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name: Screen Applicants With AI, notify HR and save them in a Google Sheet
author_name: Sarfaraz Muhammad Sajib
author_username: sarfarazmuhammad
categories: []
created_at: 2024-12-11T11:14:52.865Z
updated_at: 2024-12-11T11:14:52.865Z
url: https://n8n.io/workflows/2632
scraped_at: 2025-09-14 11:38:23
description:
  What this workflow does
  This workflow helps HR teams screen CVs with AI, store compatibility ratings in Google Sheets, and send email notifications to candidates and HR. It simplifies the recruitment process.

  CV Submission Form:
  Candidates submit their details and CV (PDF) through a web form, triggering the workflow in n8n.
  PDF Extraction & AI Rating:
  The submitted CV is processed to extract text, and AI analyzes it to generate a compatibility rating.
  Results Storage & Notifications:
  Ratings are stored in a Google Sheet for easy access and organization.
  Confirmation emails are automatically sent to both HR and the candidate.

  Setup
  Use the provided template to configure your form and connect it to n8n.
  Ensure your Google Sheets and email service integrations are active.

  Customization Instructions:

  Modify the email template to match your organization’s branding.
  Adjust the AI compatibility rating thresholds based on your requirements.
  Ensure you have updated the prompt for cv screening.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.gmail","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.extractFromFile","n8n-nodes-base.formTrigger","n8n-nodes-base.googleSheets"]

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name: INSEE Company Data Enrichment for Agile CRM (For French companies only)
author_name: Johan Denoyer
author_username: jdenoy
categories: []
created_at: 2024-12-11T16:16:09.355Z
updated_at: 2024-12-11T16:16:09.355Z
url: https://n8n.io/workflows/2633
scraped_at: 2025-09-14 11:38:24
description:
  How it works 
  1) Extracts all company entries in Agile CRM
  2) Search for company name in French INSEE OpenData database to extract address and government ID (SIREN)
  3) Updates entries with data extracted from French Insee OpenData dabase

  Workflow also has a readonly feature to make sure entry is not overwritten.

  Setup steps 
  Add your AgileCRM credentials
  Add your INSEE OpenData credentials
  Add two company custom fields in your Agile CRM (for SIREN data and ReadOnly support)

nodes_used:
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name: Spotify Sync Liked Songs to Playlist
author_name: Dustin
author_username: dh2io
categories: []
created_at: 2024-12-11T21:47:10.634Z
updated_at: 2024-12-11T21:47:10.634Z
url: https://n8n.io/workflows/2634
scraped_at: 2025-09-14 11:38:24
description:
  Short an simple:
  This Workflow will sync (add and delete) your Liked Songs to an custom playlist that can be shared.

  Setup:
  Create an app on the Spotify Developer Dashboard.
  Create Spotify Credentials - Just click on one of the Spotify Nodes in the Workflow an click on "create new credentials" and follow the guide.
  Create the Spotify Playlist that you want to sync to.
  Copy the exact name of you playlist, go into Node "Edit set Vars" and replace the value "CHANGE MEEEE" with your playlist name.
  Set your Spotify Credentiels on every Spotify Node. (Should be marekd with Yellow and Red Notes)

  Do you use Gotify?
  - No: Delete the Gotify Nodes (all the way to the right end of the Workflow)
  - Yes: Customize the Gotify Nodes to your needs.

nodes_used:
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name: Extract insights & analyse YouTube comments via AI Agent chat
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-13T12:04:55.292Z
updated_at: 2024-12-13T12:04:55.292Z
url: https://n8n.io/workflows/2636
scraped_at: 2025-09-14 11:38:23
description:
  Video Guide

  I prepared a detailed guide to help you set up your workflow effectively, enabling you to extract insights from YouTube for content generation using an AI agent.



  Youtube Link

  Who is this for?
  This workflow is ideal for content creators, marketers, and analysts looking to enhance their YouTube strategies through data-driven insights. It’s particularly beneficial for individuals wanting to understand audience preferences and improve their video content.

  What problem does this workflow solve?
  Navigating the content generation and optimization process can be complex, especially without significant audience insight. This workflow automates insights extraction from YouTube videos and comments, empowering users to create more engaging and relevant content effectively.

  What this workflow does
  The workflow integrates various APIs to gather insights from YouTube videos, enabling automated commentary analysis, video transcription, and thumbnail evaluation. The main functionalities include:
  Extracting user preferences from comments.
  Transcribing video content for enhanced understanding.
  Analyzing thumbnails via AI for maximum viewer engagement insights.

  AI Insights Extraction: Automatically pulls comments and metrics from selected YouTube creators to evaluate trends and gaps.
  Dynamic Video Planning: Uses transcriptions to help creators outline video scripts and topics based on audience interest.
  Thumbnail Assessment: Provides analysis on thumbnail designs to improve click-through rates and viewer attraction.

  Setup

  N8N Workflow
  API Setup:
     Create a Google Cloud project and enable the YouTube Data API.
     Generate an API key to be included in your workflow requests.

  YouTube Creator and Video Selection:
     Start by defining a request to identify top creators based on their video views.
     Capture the YouTube video IDs for further analysis of comments and other video metrics.

  Comment Analysis:
     Gather comments associated with the selected videos and analyze them for user insights.

  Video Transcription:
     Utilize the insights from transcriptions to formulate content plans.

  Thumbnail Analysis:
     Evaluate your video thumbnails by submitting the URL through the OpenAI API to gain insights into their effectiveness.

nodes_used:
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name: Chat Assistant (OpenAI assistant) with Postgres Memory And API Calling Capabalities
author_name: Fernanda Silva
author_username: ferzinia
categories: []
created_at: 2024-12-13T14:59:00.443Z
updated_at: 2024-12-13T14:59:00.443Z
url: https://n8n.io/workflows/2637
scraped_at: 2025-09-14 11:38:23
description:
  Workflow Description

  Your workflow is an intelligent chatbot, using ++OpenAI assistant++, integrated with a backend that supports WhatsApp Business, designed to handle various use cases such as sales and customer support. Below is a breakdown of its functionality and key components:

  Workflow Structure and Functionality

  Chat Input (Chat Trigger)
  The flow starts by receiving messages from customers via WhatsApp Business.
  Collects basic information, such as session_id, to organize interactions.

  Condition Check (If Node)
  Checks if additional customer data (e.g., name, age, dependents) is sent along with the message.
  If additional data is present, a customized prompt is generated, which includes this information. The prompt specifies that this data is for the assistant's awareness and doesn’t require a response.

  Data Preparation (Edit Fields Nodes)
  Formats customer data and the interaction details to be processed by the AI assistant.
  Compiles the customer data and their query into a single text block.

  AI Responses (OpenAI Nodes)
  The assistant’s prompt is carefully designed to guide the AI in providing accurate and relevant responses based on the customer’s query and data provided.
  Prompts describe the available functionalities, including which APIs to call and their specific purposes, helping to prevent “hallucinated” or irrelevant responses.

  Memory and Context (Postgres Chat Memory)
  Stores context and messages in continuous sessions using a database, ensuring the chatbot maintains conversation history.

  API Calls
  The workflow allows the use of APIs with any endpoints you choose, depending on your specific use case. This flexibility enables integration with various services tailored to your needs.
  The OpenAI assistant understands JSON structures, and you can define in the prompt how the responses should be formatted. This allows you to structure responses neatly for the client, ensuring clarity and professionalism.
  Make sure to describe the purpose of each endpoint in the assistant’s prompt to help guide the AI and prevent misinterpretation.

  Customer Response Delivery
  After processing and querying APIs, the generated response is sent to the backend and ultimately delivered to the customer through WhatsApp Business.

  Best Practices Implemented

  Preventing Hallucinations**  
    Every API has a clear description in its prompt, ensuring the AI understands its intended use case.

  Versatile Functionality**  
    The chatbot is modular and flexible, capable of handling both sales and general customer inquiries.

  Context Persistence**  
    By utilizing persistent memory, the flow maintains continuous interaction context, which is crucial for longer conversations or follow-up queries.

  Additional Recommendations

  Include practical examples in the assistant’s prompt, such as frequently asked questions or decision-making flows based on API calls.
  Ensure all responses align with the customer’s objectives (e.g., making a purchase or resolving technical queries).
  Log interactions in detail for future analysis and workflow optimization.

  This workflow provides a solid foundation for a robust and multifunctional virtual assistant 🚀

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.memoryPostgresChat","n8n-nodes-base.mySqlTool","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi"]

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name: Analyze tradingview.com charts with Chrome extension, N8N and OpenAI
author_name: Hans Blaauw
author_username: thingsio
categories: []
created_at: 2024-12-16T10:08:04.476Z
updated_at: 2024-12-16T10:08:04.476Z
url: https://n8n.io/workflows/2642
scraped_at: 2025-09-14 11:38:21
description:
  This flow is supported by a Chrome plugin created with Cursor AI.

  The idea was to create a Chrome plugin and a backend service in N8N to do chart analytics with OpenAI. It's a good sample on how to submit a screenshot from the browser to N8N.

  Who is it for?
  N8N developers who want to learn about using a Chrome plugin, an N8N webhook and OpenAI.

  What opportunity does it present?
  This sample opens up a whole range of N8N connected Chrome extensions that can analyze screenshots by using OpenAI.

  What this workflow does?
  The workflow contains:

  a webhook trigger
  an OpenAI node with GPT-4O-MINI and Analyze Image selected
  a response node to send back the Text that was created after analysing the screenshot.




  All this is needed to talk to the Chrome extension which is created with Cursor AI.

  The idea is to visit the tradingview.com crypto charts, click the Chrome plugin and get back analytics about the shown chart in understandable language. This is driven by the N8N flow.

  With the new image analytics capabilities of OpenAI this opens up a world of opportunities.

  Requirements/setup

  OpenAI API key
  Cursor AI installed
  The Chrome extension. Download
  The N8N JSON code. Download

  How to customize it to your needs?
  Both the Chrome extension and N8N flow can be adapted to use on other websites. You can consider:

  analyzing a financial screen and ask questions about the data shown
  analyzing other charts
  extending the N8N workflow with other AI nodes

  With AI and image analytics the sky is the limit and in some cases it saves you from creating complex API integrations.

  Download Chrome extension

nodes_used:
  ["n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Intelligent Web Query and Semantic Re-Ranking Flow using Brave and Google Gemini
author_name: Mind-Front
author_username: mind-front
categories: []
created_at: 2024-12-16T13:55:44.230Z
updated_at: 2024-12-16T13:55:44.230Z
url: https://n8n.io/workflows/2643
scraped_at: 2025-09-14 11:38:23
description:
  Workflow Description


  This workflow is a powerful, fully automated web query and semantic reranking system that allows users to perform precise, detailed searches, intelligently rank search results and provide high-quality, structured output. Built with AI-powered components, the workflow leverages semantic query generation, result re-ranking, and real-time reporting to deliver actionable insights.

  It is particularly well-suited for real-time data retrieval, market research, and any domain requiring automated yet customizable search result processing.

  How It Works

  Webhook Integration for Input:  
     The workflow begins with a Webhook Node that captures the user's search query as input, enabling seamless integration with other systems.  

  Step 1: Semantic Query Generation (Powered by "Semantic Search - Query Maker"):  
     Using AI (Google Gemini), the initial query is refined and transformed into a context-aware, expert-level search query.  
     The process ensures that the search engine retrieves the most relevant and precise results.

  Step 2: Web Search Execution:  
     A free Brave Search API processes the refined query to fetch search results, ensuring speed and cost efficiency.

  Step 3: Semantic Re-Ranking of Results (Powered by "Semantic Search - Result Re-Ranker"):  
     The workflow reranks the search results based on relevance to the original question, prioritizing the most relevant URLs dynamically.  
     Results are passed through AI-powered intelligent reranking to ensure the final output reflects optimal relevance and quality.

  Step 4: Structured Output Generation:  
     Results are converted into a well-structured, organized JSON format, ranking the top 10 search results with their titles, links, and descriptions.  
     Missing ranks (if fewer than 10 results) are handled gracefully with placeholders, ensuring consistency.

  Step 5: Real-Time Reporting:  
     The reranked search results are sent back to the user or integrated system via the Webhook Node in a JSON-formatted response.  
     Reports are highly structured and ready for downstream processing or consumption.

  Key Features

  AI-Powered Query Refinement:  
     Transforms basic queries into detailed, expert-level search terms for optimal results.

  Dual-Stage Semantic Search:  
     Combines query generation and result reranking for precise, high-relevance outputs.

  Top 10 Result Reranking:  
     Dynamically ranks and organizes the top 10 results based on semantic relevance to the query.

  Customizable Integration:  
     Fully modifiable for alternative APIs or integrations, such as other search engines or custom ranking logic.

  JSON-Formatted Structured Results:  
     Outputs reranked results in a standardized format, ideal for integration into systems requiring machine-readable data.

  Webhook-Based Flexibility:  
     Works seamlessly with Webhook inputs for easy deployment in diverse workflows.

  Cost-Effective API Usage:  
     Pre-integrated with the free Brave Search API, minimizing operational costs while delivering accurate search results.

  Instructions for API Setup

  Brave Search API:  
     Visit api.search.brave.com to obtain a free-tier API key for web search.  
  AI Integration (Google Gemini):  
     Visit Google AI Studio and generate an API key for semantic query generation and reranking.  
  Webhook Configuration:  
     Set up the input Webhook to capture search queries and the output Webhook to deliver reranked results.

  Why Choose This Workflow?

  Precision and Relevance**: Combines AI-based query generation with advanced reranking for accurate results.  
  Fully Customizable**: Easily adapt the workflow to alternative APIs, search engines, or ranking logic.  
  Real-Time Insights**: Provides structured, real-time output ready for immediate use.  
  Scalable and Modular**: Ideal for businesses, researchers, and data analysts needing a robust, repeatable solution.

  Tags  
  AI Workflow, Semantic Search, Query Refinement, Search Result Reranking, Real-Time Search, Web Search Automation, Google Search, Brave Search, News Search, API Integration, Market Research, Competitive Intelligence, Business Intelligence,Google Gemini, Anthropic Claude, OpenAI, GPT, LLM

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.dateTime","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.respondToWebhook","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatAnthropic","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Flux Dev Image Generation (Fal.ai) to Google Drive
author_name: Sira Ekabut
author_username: siraekabut
categories: []
created_at: 2024-12-16T16:52:49.861Z
updated_at: 2024-12-16T16:52:49.861Z
url: https://n8n.io/workflows/2644
scraped_at: 2025-09-14 11:38:22
description:
  This workflow automates AI-based image generation using the Fal.ai Flux API. Define custom prompts, image parameters, and effortlessly generate, monitor, and save the output directly to Google Drive. Streamline your creative automation with ease and precision.

  Who is this for?

  This template is for content creators, developers, automation experts, and creative professionals looking to integrate AI-based image generation into their workflows. It’s ideal for generating custom visuals with the Fal.ai Flux API and automating storage in Google Drive.

  What problem is this workflow solving?

  Manually generating AI-based images, checking their status, and saving results can be tedious. This workflow automates the entire process — from requesting image generation, monitoring its progress, downloading the result, and saving it directly to a Google Drive folder.

  What this workflow does
  	Sets Custom Image Parameters: Allows you to define the prompt, resolution, guidance scale, and steps for AI image generation.
  	Sends a Request to Fal.ai: Initiates the image generation process using the Fal.ai Flux API.
  	Monitors Image Status: Checks for completion and waits if needed.
  	Downloads the Generated Image: Fetches the completed image once ready.
  	Saves to Google Drive: Automatically uploads the generated image to a specified Google Drive folder.

  Setup
  	1.	Prerequisites:
  	•	Fal.ai API Key: Obtain it from the Fal.ai platform and set it as the Authorization header in HTTP Header Auth credentials.
  	•	Google Drive OAuth Credentials: Connect your Google Drive account in n8n.
  	2.	Configuration:
  	•	Update the “Edit Fields” node with your desired image parameters:
  	•	Prompt: Describe the image (e.g., “Thai young woman net idol 25 yrs old, walking on the street”).
  	•	Width/Height: Define image resolution (default: 1024x768).
  	•	Steps: Number of inference steps (e.g., 30).
  	•	Guidance Scale: Controls image adherence to the prompt (e.g., 3.5).
  	•	Set your Google Drive folder ID in the “Google Drive” node to save the image.
  	3.	Run the Workflow:
  	•	Trigger the workflow manually to generate the image.
  	•	The workflow waits, checks status, and saves the final output seamlessly.

  Customization
  	•	Modify Image Parameters: Adjust the prompt, resolution, steps, and guidance scale in the “Edit Fields” node.
  	•	Change Storage Location: Update the Google Drive node with a different folder ID.
  	•	Add Notifications: Integrate an email or messaging node to alert you when the image is ready.
  	•	Additional Outputs: Expand the workflow to send the generated image to Slack, Dropbox, or other platforms.

  This workflow streamlines AI-based image generation and storage, offering flexibility and customization for creative automation.

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

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name: Automatically prune n8n execution history
author_name: Danger
author_username: dangerblack
categories: []
created_at: 2024-12-17T09:10:02.525Z
updated_at: 2024-12-17T09:10:02.525Z
url: https://n8n.io/workflows/2646
scraped_at: 2025-09-14 11:38:24
description:
  Automated Execution Pruning

  This workflow is designed to help you manage and optimize your n8n instance by automatically pruning old workflow executions, ensuring a cleaner environment and improved performance. You can customize the retention period to suit your needs.

  Key Features:
  Configurable Retention Period:  
     The workflow is preconfigured to delete workflow executions older than 10 days. You can easily adjust this duration by modifying the condition in the If node.

  Daily Automation:  
     Using the Schedule Trigger, the workflow runs daily at the specified time (default: 4:44 AM), retrieving all workflow executions and identifying those that are older than the defined retention period.

  On-Demand Testing:  
     The Manual Trigger allows you to test the workflow at any time, ensuring everything is working as expected.

  Decision Making:  
     The If node evaluates each execution based on its start date and determines whether it should be deleted or retained.

  Execution Pruning:  
     Delete Action: Executions meeting the criteria are removed via the Delete Execution node.  
     No-Operation: Executions that don't meet the criteria remain untouched.

  Workflow Nodes:
  Manual Trigger: Enables on-demand testing of the workflow.  
  Schedule Trigger: Runs the workflow daily at the configured time.  
  n8n List Execution: Fetches all executions in your n8n instance.  
  If Node: Compares the execution's start date with the configured retention period.  
  Delete Execution: Deletes executions older than the specified retention period.  
  No Operation: Serves as a placeholder for executions that don't meet the pruning criteria.  

  How to Customize:
  Retention Period**:  
     Update the If node's condition to modify the retention period. For instance, change 10 * 24 * 60 * 60 * 1000 to the desired number of days in milliseconds.
     
  Schedule**:  
     Adjust the timing of the Schedule Trigger to match your preferred automation schedule.

  This workflow ensures your instance remains efficient by keeping only the relevant execution logs. Use it to maintain a streamlined and clutter-free environment effortlessly.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.n8n","n8n-nodes-base.n8n","n8n-nodes-base.if","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.n8n","n8n-nodes-base.noOp"]

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name: Sentiment Analysis Tracking on Support Issues with Linear and Slack
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-17T10:19:26.716Z
updated_at: 2024-12-17T10:19:26.716Z
url: https://n8n.io/workflows/2647
scraped_at: 2025-09-14 11:38:23
description:
  This n8n template monitors active support issues in Linear.app to track the mood of their ongoing conversation between reporter and assignee using Sentiment Analysis. When sentiment dips into the negative, a notification is sent via Slack to alert the team.

  How it works
  A scheduled trigger is used to fetch recently updated issues in Linear using the GraphQL node.
  Each issue's comments thread is passed into a simple Information Extractor node to identify the overall sentiment.
  The resulting sentiment analysis combined with the some issue details are uploaded to Airtable for review.
  When the template is re-run at a later date, each issue is re-analysed for sentiment
  Each issue's new sentiment state is saved to the airtable whilst its previous state is moved to the "previous sentiment" column.
  An Airtable trigger is used to watch for recently updated rows
  Each matching Airtable row is filtered to check if it has a previous non-negative state but now has a negative state in its current sentiment.
  The results are sent via notification to a team slack channel for priority.

  Check out the sample Airtable here: https://airtable.com/appViDaeaFw4qv9La/shrq6HgeYzpW6uwXL

  How to use
  Modify the GraphQL filter to fetch issues to a relevant issue type, team or person.
  Update the Slack channel to ensure messages are sent to the correct location or persons.
  The Airtable also serves to give a snapshot of Sentiment across support tickets for a given period. It's possible to use this to assess the daily operations.

  Requirements
  Linear for issue tracking (but feel free to use another system if preferred)
  Airtable for Database
  OpenAI for LLM and Sentiment Analysis

  Customising the workflow
  Add more granular levels of sentiment to reduce the number of alerts.
  Explore different types of sentiment based on issue types and customer types. This may help prioritise alerts and response.
  Run across teams or categories of issues to get an overview of sentiment across the support organisation.

nodes_used:
  ["n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtableTrigger","n8n-nodes-base.switch","n8n-nodes-base.graphql","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.removeDuplicates","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Automate Blog Creation in Brand Voice with AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-17T10:37:34.169Z
updated_at: 2024-12-17T10:37:34.169Z
url: https://n8n.io/workflows/2648
scraped_at: 2025-09-14 11:38:21
description:
  This n8n template demonstrates a simple approach to using AI to automate the generation of blog content which aligns to your organisation's brand voice and style by using examples of previously published articles.

  In a way, it's quick and dirty "training" which can get your automated content generation strategy up and running for very little effort and cost whilst you evaluate our AI content pipeline.

  How it works
  In this demonstration, the n8n.io blog is used as the source of existing published content and 5 of the latest articles are imported via the HTTP node.
  The HTML node is extract the article  bodies which are then converted to markdown for our LLMs.
  We use LLM nodes to (1) understand the article structure and writing style and (2) identify the brand voice characteristics used in the posts.
  These are then used as guidelines in our final LLM node when generating new articles.
  Finally, a draft is saved to Wordpress for human editors to review or use as starting point for their own articles.

  How to use
  Update Step 1 to fetch data from your desired blog or change to fetch existing content in a different way.
  Update Step 5 to provide your new article instruction. For optimal output, theme topics relevant to your brand.

  Requirements
  A source of text-heavy content is required to accurately breakdown the brand voice and article style. Don't have your own? Maybe try your competitors?
  OpenAI for LLM - though I recommend exploring other models which may give subjectively better results.
  Wordpress for blog but feel free to use other preferred publishing platforms.

  Customising this workflow
  Ideally, you'd want to "train" your agent on material which is similar to your output ie. your social media post may not get the best results from your blog content due to differing formats.
  Typically, this brand voice extraction exercise should run once and then be cached somewhere for reuse later. This would save on generation time and overall cost of the workflow. 

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.limit","n8n-nodes-base.html","n8n-nodes-base.aggregate","n8n-nodes-base.merge","n8n-nodes-base.wordpress","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.markdown","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

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name: Prompt-based Object Detection with Gemini 2.0
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-17T11:53:59.121Z
updated_at: 2024-12-17T11:53:59.121Z
url: https://n8n.io/workflows/2649
scraped_at: 2025-09-14 11:38:23
description:
  This n8n template demonstrates how to get started with Gemini 2.0's new Bounding Box detection capabilities in your workflows.

  The key difference being this enables prompt-based object detection for images which is pretty powerful for things like contextual search over an image. eg. "Put a bounding box around all adults with children in this image" or "Put a bounding box around cars parked out of bounds of a parking space".

  How it works
  An image is downloaded via the HTTP node and an "Edit Image" node is used to extract the file's width and height.
  The image is then given to the Gemini 2.0 API to parse and return coordinates of the bounding box of the requested subjects. In this demo, we've asked for the AI to identify all bunnies.
  The coordinates are then rescaled with the original image's width and height to correctl align them.
  Finally to measure the accuracy of the object detection, we use the "Edit Image" node to draw the bounding boxes onto the original image.

  How to use
  Really up to the imagination! Perhaps a form of grounding for evidence based workflows or a higher form of image search can be built.

  Requirements
  Google Gemini for LLM

  Customising the workflow
  This template is just a demonstration of an experimental version of Gemini 2.0. It is recommended to wait for Gemini 2.0 to come out of this stage before using in production.

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

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name: Extract Information from a Logo Sheet using forms, AI, Google Sheet and Airtable
author_name: Marcel Claus-Ahrens
author_username: geckse
categories: []
created_at: 2024-12-17T12:35:08.190Z
updated_at: 2024-12-17T12:35:08.190Z
url: https://n8n.io/workflows/2650
scraped_at: 2025-09-14 11:38:24
description:
  Instructions

  This automation enables you to just upload any Image (via Form) of a Logo Sheet, containing multiple Images of Product Logos (most likely) which brings them in some context to one another. 

  After submitting an AI-Agent eats that Logo Sheet, turning it into an List of "Productname" and "Attributes", also checks if Tools are kind of similar to another, given the Context of the Image.

  We utilize AI Vision capabilities for that. NOTE: It might not be able to extract all informations. For a "upload and forget it" Workflow it works for me. You can even run it multiple times, to be sure. 

  But if you need to make sure it extracts everything you might need to think about an Multi-Agent Setup with Validation-Agent Steps.

  Once the Agent finishes the extraction, it will traditionally and deterministicly add those Attributes to Airtable (Creates those, if not already existing.) and also Upserts the Tool Informations.

  It uses MD5 Hashes for turning Product Names into.. something fancy really, you could also use it without that, but I wanted to have something that looks atleast like an ID.  

  Setup

  Set Up the Airtable like shown below.
  Update and set Credentials for all Airtable Nodes.
  Check or Adjust the Prompt of the Agent matching your use-case.
  Activate the Workflow. 
  Open the Form (default: https://your-n8n.io/form/logo-sheet-feeder)
  Enjoy growing your Airtable.

    
  Enjoy the workflow! ❤️  
  let the work flow — Workflow Automation & Development

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.airtable","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.crypto","n8n-nodes-base.airtable","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.merge","n8n-nodes-base.crypto","n8n-nodes-base.airtable","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.airtable","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.html","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: AI Agent for realtime insights on meetings
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-17T16:48:41.040Z
updated_at: 2024-12-17T16:48:41.040Z
url: https://n8n.io/workflows/2651
scraped_at: 2025-09-14 11:38:21
description:
  Video Guide


  I prepared a detailed guide explaining how to build an AI-powered meeting assistant that provides real-time transcription and insights during virtual meetings.



  Youtube Link

  Who is this for?
  This workflow is ideal for business professionals, project managers, and team leaders who require effective transcription of meetings for improved documentation and note-taking. It's particularly beneficial for those who conduct frequent virtual meetings across various platforms like Zoom and Google Meet.

  What problem does this workflow solve?
  Transcribing meetings manually can be tedious and prone to error. This workflow automates the transcription process in real-time, ensuring that key discussions and decisions are accurately captured and easily accessible for later review, thus enhancing productivity and clarity in communications.

  What this workflow does
  The workflow employs an AI-powered assistant to join virtual meetings and capture discussions through real-time transcription. Key functionalities include:
  Automatic joining of meetings on platforms like Zoom, Google Meet, and others with the ability to provide real-time transcription.
  Integration with transcription APIs (e.g., AssemblyAI) to deliver seamless and accurate capture of dialogue.
  Structuring and storing transcriptions efficiently in a database for easy retrieval and analysis.

  Real-Time Transcription: The assistant captures audio during meetings and transcribes it in real-time, allowing participants to focus on discussions.
  Keyword Recognition: Key phrases can trigger specific actions, such as noting important points or making prompts to the assistant.
  Structured Data Management: The assistant maintains a database of transcriptions linked to meeting details for organized storage and quick access later.

  Setup

  Preparation

  Create Recall.ai API key
  Setup Supabase account and table
  create table
    public.data (
      id uuid not null default gen_random_uuid (),
      date_created timestamp with time zone not null default (now() at time zone 'utc'::text),
      input jsonb null,
      output jsonb null,
      constraint data_pkey primary key (id),
    ) tablespace pg_default;

  Create OpenAI API key

  Development

  Bot Creation: 
     Use a node to create the bot that will join meetings. Provide the meeting URL and set transcription options within the API request.

  Authentication: 
     Configure authentication settings via a Bearer token for interacting with your transcription service.

  Webhook Setup: 
     Create a webhook to receive real-time transcription updates, ensuring timely data capture during meetings.

  Join Meeting: 
     Set the bot to join the specified meeting and actively listen to capture conversations.

  Transcription Handling: 
     Combine transcription fragments into cohesive sentences and manage dialog arrays for coherence.

  Trigger Actions on Keywords: 
     Set up keyword recognition that can initiate requests to the OpenAI API for additional interactions based on captured dialogue.

  Output and Summary Generation: 
     Produce insights and summary notes from the transcriptions that can be stored back into the database for future reference.

nodes_used:
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name: Backup your workflows to GitHub
author_name: Solomon
author_username: solomon
categories: []
created_at: 2024-12-18T15:05:17.137Z
updated_at: 2024-12-18T15:05:17.137Z
url: https://n8n.io/workflows/2652
scraped_at: 2025-09-14 11:38:22
description:
  Based on Jonathan's work. Check out his templates.

  How it works
  This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows.

  It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will:
  update the file on GitHub if it exists;
  create a new file if it doesn't exist;
  ignore if it's the same.

  Who is this for?
  People wanting to backup their workflows outside the server for safety purposes or to migrate to another server.

  Check out my other templates
  👉 https://n8n.io/creators/solomon/

nodes_used:
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name: AI Data Analyst Agent and Visualization Agent for Large Spreadsheets
author_name: Derek Cheung
author_username: derekcheungsa
categories: []
created_at: 2024-12-18T23:58:03.508Z
updated_at: 2024-12-18T23:58:03.508Z
url: https://n8n.io/workflows/2653
scraped_at: 2025-09-14 11:38:23
description:
  Purpose of workflow:

  This workflow transforms spreadsheet data into an interactive, AI-powered knowledge base that enables users to gain deep insights through natural language queries, searchability, and comparative analysis.

  How it works:

  Data Storage & Integration:
  Spreadsheet data is imported into a no-code database (NocoDB)
  System connects with an AI data analyst agent
  Agent accesses table metadata and column information

  Query Processing:
  Users input natural language questions
  AI agent interprets queries and converts them to database filters
  System retrieves relevant data using filter formulas
  AI synthesizes responses with analysis and insights

  Advanced Capabilities:
  Performs comparative analysis across multiple data points
  Handles complex multi-part queries

  Automatically creates visualizations:
  Visualization AI Agent figures out the data and the chart type and generates professional visualization using Quickchart


  Step by step setup:
  Create account on nocodb.com
  Create table by importing csv, copy table id
  Create API token https://app.nocodb.com/#/account/tokens
  In workflow, settings node, update with table id
  In NocoDB tool node, setup authentication with API token created in step 3
  Specify the workspace and base fields after connecting to NocoDB 

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  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.set","n8n-nodes-base.nocoDbTool","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote"]

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name: Vector Database as a Big Data Analysis Tool for AI Agents [1/3 anomaly][1/2 KNN]
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2024-12-19T09:36:22.024Z
updated_at: 2024-12-19T09:36:22.024Z
url: https://n8n.io/workflows/2654
scraped_at: 2025-09-14 11:38:23
description:
  Vector Database as a Big Data Analysis Tool for AI Agents

  Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n".

  This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases.

  Uploading (image) datasets to Qdrant
  Set up meta-variables for anomaly detection in Qdrant
  Anomaly detection tool
  KNN classifier tool

  For anomaly detection
  1. This is the first pipeline to upload an image dataset to Qdrant.
  The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection.
  The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset.

  For KNN (k nearest neighbours) classification
  1. This is the first pipeline to upload an image dataset to Qdrant.
  The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset.

  To recreate both
  You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. 

  [This workflow] Batch Uploading Images Dataset to Qdrant 
  This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant, also in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are).

  First, check for an existing Qdrant collection to use; otherwise, create it here. Additionally, when creating the collection, we'll create a payload index, which is required for a particular type of Qdrant requests we will use later.
  Next, import all (dataset) images from Google Cloud Storage but keep only non-tomato-related ones (for anomaly detection testing).
  Create (per batch) embeddings for all imported images using the Voyage AI multimodal embeddings API.
  Finally, upload the resulting embeddings and image descriptors to Qdrant via batch upload.

nodes_used:
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name: Vector Database as a Big Data Analysis Tool for AI Agents [2/3 - anomaly]
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2024-12-19T09:41:43.006Z
updated_at: 2024-12-19T09:41:43.006Z
url: https://n8n.io/workflows/2655
scraped_at: 2025-09-14 11:38:24
description:
  Vector Database as a Big Data Analysis Tool for AI Agents

  Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n".

  This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases.

  Uploading (image) datasets to Qdrant
  Set up meta-variables for anomaly detection in Qdrant
  Anomaly detection tool
  KNN classifier tool

  For anomaly detection
  The first pipeline to upload an image dataset to Qdrant.
  2. This is the second pipeline to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection.
  The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset.

  For KNN (k nearest neighbours) classification
  The first pipeline to upload an image dataset to Qdrant.
  The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset.

  To recreate both
  You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. 

  [This workflow] Setting Up Cluster (Class) Centres & Cluster (Class) Threshold Scores for Anomaly Detection
  Preparatory workflow to set cluster centres and cluster threshold scores so anomalies can be detected based on these thresholds.
  Here, we're using two approaches to set up these centres: the "distance matrix approach" and the "multimodal embedding model approach".

nodes_used:
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name: Vector Database as a Big Data Analysis Tool for AI Agents [3/3 - anomaly]
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2024-12-19T09:46:01.013Z
updated_at: 2024-12-19T09:46:01.013Z
url: https://n8n.io/workflows/2656
scraped_at: 2025-09-14 11:38:23
description:
  Vector Database as a Big Data Analysis Tool for AI Agents

  Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n".

  This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases.

  Uploading (image) datasets to Qdrant
  Set up meta-variables for anomaly detection in Qdrant
  Anomaly detection tool
  KNN classifier tool

  For anomaly detection
  The first pipeline to upload an image dataset to Qdrant.
  The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection.
  3. This is the third pipeline --- the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset.

  For KNN (k nearest neighbours) classification
  The first pipeline to upload an image dataset to Qdrant.
  The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset.

  To recreate both
  You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. 

  [This workflow] Anomaly Detection Tool
  This is the tool that can be used directly for anomalous images (crops) detection. 
  It takes as input (any) image URL and returns a text message telling if whatever this image depicts is anomalous to the crop dataset stored in Qdrant. 

  An Image URL is received via the Execute Workflow Trigger, which is used to generate embedding vectors using the Voyage AI Embeddings API.
  The returned vectors are used to query the Qdrant collection to determine if the given crop is known by comparing it to threshold scores of each image class (crop type).
  If the image scores lower than all thresholds, then the image is considered an anomaly for the dataset.

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name: Vector Database as a Big Data Analysis Tool for AI Agents [2/2 KNN]
author_name: Jenny
author_username: mrscoopers
categories: []
created_at: 2024-12-19T09:50:11.092Z
updated_at: 2024-12-19T09:50:11.092Z
url: https://n8n.io/workflows/2657
scraped_at: 2025-09-14 11:38:24
description:
  Vector Database as a Big Data Analysis Tool for AI Agents

  Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n".

  This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases.

  Uploading (image) datasets to Qdrant
  Set up meta-variables for anomaly detection in Qdrant
  Anomaly detection tool
  KNN classifier tool

  For anomaly detection
  The first pipeline to upload an image dataset to Qdrant.
  The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection.
  The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset.

  For KNN (k nearest neighbours) classification
  The first pipeline to upload an image dataset to Qdrant.
  This pipeline is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset.

  To recreate both
  You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage.

  [This workflow] KNN classification tool
  This tool takes any image URL, and as output, it returns a class of the object on the image based on the image uploaded to the Qdrant dataset (lands).

  An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage AI Multimodal Embeddings API to fetch its embedding.
  The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes.
  Majority voting is done for classes of neighbouring images.
  A loop is used to resolve scenarios where there is a tie in Majority Voting, and we increase the number of neighbours to retrieve.
  When the loop finally resolves, the identified class is returned to the calling workflow.

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name: API Schema Extractor
author_name: Polina Medvedieva
author_username: polina-n8n
categories: []
created_at: 2024-12-19T12:39:56.130Z
updated_at: 2024-12-19T12:39:56.130Z
url: https://n8n.io/workflows/2658
scraped_at: 2025-09-14 11:38:21
description:
  This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet.

  🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies.

  How it works
  Stage 1 - Research:
  Fetches pending services from a Google Sheet
  Uses Google search to find API documentation
  Employs Apify for web scraping to filter relevant pages
  Stores webpage contents and metadata in Qdrant (vector database)
  Updates progress status in Google Sheet (pending, ok, or error)

  Stage 2 - Extraction:
  Processes services that completed research successfully
  Queries vector store to identify products and offerings
  Further queries for relevant API documentation
  Uses Gemini (LLM) to extract API operations
  Records extracted operations in Google Sheet
  Updates progress status (pending, ok, or error)

  Stage 3 - Generation:
  Takes services with successful extraction
  Retrieves all API operations from the database
  Combines and groups operations into a custom schema
  Uploads final schema to Google Drive
  Updates final status in sheet with file location

  Ideal for:
  Development teams needing to catalog multiple APIs
  API documentation initiatives
  Creating standardized API schema collections
  Automating API discovery and documentation

  Accounts required:
  Google account (for Sheets and Drive access)
  Apify account (for web scraping)
  Qdrant database
  Gemini API access

  Set up instructions:
  Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. 
  Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini.
  Ensure proper permissions for Google Drive access.

nodes_used:
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name: Upload files via n8n form and save them to Digital Ocean Spaces
author_name: Alfred Nutile
author_username: dailyaistudio
nodes_used: ["n8n-nodes-base.formTrigger","n8n-nodes-base.s3","n8n-nodes-base.form"]
categories: []
created_at: 2024-12-19T19:35:17.854Z
updated_at: 2024-12-19T19:35:17.854Z
url: https://n8n.io/workflows/2660
scraped_at: 2025-09-14 11:38:23
description:
  How it works
  This workflow provides a streamlined process for uploading files to Digital Ocean Spaces, making them publicly accessible. The process happens in three main steps:

  User submits the form with file, in this case I needed it to upload images I use in my seo tags.
  File is automatically uploaded to Digital Ocean Spaces using S3-compatible storage
  Form completion confirmation is provided

  Setup steps
  Initial setup typically takes 5-10 minutes
  Configure your Digital Ocean Spaces credentials and bucket settings
  Test the upload functionality with a small sample file
  Verify public access permissions are working as expected

  Important notes
  Credentials are tricky check the screenshot above for how I set the url, bucket etc.
  I am just using the S3 Node
  Set the ACL as seen below

  Troubleshooting
  Bucket name might be incorrect
  Region Wrong
  Check Space permissions if uploads fail
  Verify API credentials are correctly configured


  You can see a video here. (live in 24 hours)
  https://youtu.be/pYOpy3Ntt1o

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name: Parse PDF with LlamaParse and save to Airtable
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-19T21:56:14.136Z
updated_at: 2024-12-19T21:56:14.136Z
url: https://n8n.io/workflows/2661
scraped_at: 2025-09-14 11:38:23
description:
  Video Guide

  I prepared a comprehensive guide detailing how to automate the parsing of invoices using n8n and LlamaParse, seamlessly capturing and storing vital billing information.



  Youtube Link

  Who is this for?
  This workflow is ideal for finance teams, accountants, and business operations managers who need to streamline invoice processing. It is particularly helpful for organizations seeking to reduce manual entry errors and improve efficiency in managing billing information.

  What problem does this workflow solve?
  Manually processing invoices can be time-consuming and error-prone. This automation eliminates the need for manual data entry by capturing invoice details directly from uploaded documents and storing structured data efficiently. This enhances productivity and accuracy across financial operations.

  What this workflow does
  The workflow leverages n8n and LlamaParse to automatically detect new invoices in a designated Google Drive folder, parse essential billing details, and store the extracted data in a structured format. The key functionalities include:
  Real-time detection of new invoices via Google Drive triggers.
  Automated HTTP requests to initiate parsing through Lama Cloud.
  Structured storage of invoice details and line items in a database for future reference.

  Google Drive Integration: Monitors a specific folder in Google Drive for new invoice uploads.
  Parsing with LlamaParse: Automatically sends invoices for parsing and processes results through webhooks.
  Data Storage in Airtable: Creates records for invoices and their associated line items, allowing for detailed tracking.

  Setup

  N8N Workflow
  Google Drive Trigger: 
     Set up a trigger to detect new files in a specified folder dedicated to invoices.

  File Upload to LlamaParse: 
     Create an HTTP request that sends the invoice file to LlamaParse for parsing, including relevant header settings and webhook URL.

  Webhook Processing: 
     Establish a webhook node to handle parsed results from LlamaParse, extracting needed invoice details effectively.

  Invoice Record Creation: 
     Create initial records for invoices in your database using the parsed details received from the webhook.

  Line Item Processing: 
     Transform string data into structured line item arrays and create individual records for each item linked to the main invoice.

nodes_used:
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name: Generate 9:16 Images from Content and Brand Guidelines
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2024-12-19T23:45:55.512Z
updated_at: 2024-12-19T23:45:55.512Z
url: https://n8n.io/workflows/2662
scraped_at: 2025-09-14 11:38:22
description:
  Overview
  This n8n workflow automates the creation of 9:16 aspect ratio images optimized for short-form video content and thumbnails. It integrates multiple tools to retrieve content, generate scripts, and create AI-generated imagery.

  Key Features
  Trigger Workflow Manually  
     The workflow starts when triggered manually in n8n.

  Retrieve Brand Guidelines  
     Fetch brand elements like style, tone, and guidelines from Airtable.

  SEO Keywords and Blog Post Retrieval  
     Retrieves blog posts and associated SEO keywords from Airtable to form the basis of image content.

  Content Preparation  
     Uses GPT-4 to prepare a 4-scene script and thumbnail prompts for short-form videos.

  AI Image Generation  
     Uses Leonardo.ai API to generate:
       Thumbnail Images
       Scene-specific Images (9:16 Aspect Ratio)

  Airtable Asset Management  
     Generated assets (images) are saved back into Airtable with metadata like URLs and file sizes.

  Tools and Integrations
  n8n**: Workflow automation platform.
  OpenAI**: Generates scripts and prompts (GPT-4O-MINI).
  Leonardo.ai**: AI tool for improving prompts and generating high-quality images.
  Airtable**: Used as a data source for brand guidelines, blog posts, and to store generated assets.

  Workflow Steps
  Manual Trigger  
     Initiate the workflow.

  Retrieve Brand and SEO Guidelines  
     Fetch essential brand elements like tone, style, and keywords.

  Filter and Fetch Blog Content  
     Search for blog posts relevant to selected SEO keywords.

  Script Preparation  
     Use GPT-4 to generate a script with image prompts for scenes and thumbnails.

  Image Generation  
     Call Leonardo.ai to create:
       Scene Images in 9:16 Aspect Ratio.
       A Thumbnail Image with an improved prompt.

  Store Assets  
     Save generated assets (images) to Airtable for future use.

  Workflow Structure
  Nodes Breakdown:
  Manual Trigger**: Start the workflow.
  Get Brand Guidelines**: Pull brand-related information (style, tagline, tone, etc.) from Airtable.
  Set Guidelines**: Prepare fetched data.
  Get SEO Keywords**: Retrieve keywords to filter relevant content.
  Keyword Filter*: Filter results for specified keywords (e.g., *"AI Automation").
  Script Prep**: Generate 4-scene scripts and prompts with GPT-4.
  Leo - Improve Prompt**: Improve image prompts for clarity and detail.
  Leo - Generate Image**: Create AI-generated images for scenes and thumbnails.
  Wait Nodes**: Ensures Leonardo image generation is complete.
  Add Asset Info**: Store the generated images back into Airtable with metadata.

  API Credentials Required
  Ensure the following credentials are configured in n8n:
  OpenAI API Key
  Leonardo.ai API Key
  Airtable API Token

  Output
  Generated Images**: High-quality AI-generated images with a 9:16 aspect ratio.
  Saved Metadata**: Asset details (URLs, sizes, types) stored in Airtable.

  Usage
  Import this workflow into n8n.
  Set up your Airtable API, Leonardo.ai API, and OpenAI API credentials.
  Run the workflow manually.
  Monitor image generation and check the Airtable output for results.

  Tags
  OpenAI**
  RunwayML**
  Leonardo**
  Airtable**
  Video Automation**

  Author
  AlexK1919  
  AI-Native Workflow Architect  
  More Workflow Templates  
  YouTube Channel  
  Connect with Alex

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name: YouTube Report Generator
author_name: Mauricio Perera
author_username: rckflr
categories: []
created_at: 2024-12-20T02:18:31.382Z
updated_at: 2024-12-20T02:18:31.382Z
url: https://n8n.io/workflows/2663
scraped_at: 2025-09-14 11:38:24
description:
  YouTube Subtitles Report Generator

  Overview

  This template enables users to generate analytical reports from YouTube video subtitles, providing insights into the thematic content of the video. Designed for efficiency and simplicity, it processes video subtitles without requiring an API key, making it an accessible solution for content analysis. The system assumes videos already have subtitles available, excluding live streams and videos without subtitle data.

  Key Features

  Trigger Webhook: Seamlessly receive video IDs through a webhook trigger.
  Fetch Video HTML: Retrieves the video’s HTML content directly from YouTube.
  Extract Subtitles URLs: Processes the HTML content to find and decode subtitle URLs.
  Fetch Subtitles Content: Downloads the subtitles from the decoded URLs in XML format.
  Generate Analytical Report: Utilizes an AI model to summarize and analyze the thematic essence of video content.
     The system supports models such as Google Gemini, OpenAI, and other compatible language models. 
     The quality of the results may vary depending on the model used, with better models providing faster and more accurate summaries.
     The default prompt focuses on identifying and summarizing the main theme of the video while excluding content related to promotions, subscriptions, or sponsored content.
  Return Analytical Report: Delivers concise analytical reports as the final response to the webhook, suitable for various use cases like research, content creation, or consumption as plain text.

  Setup Instructions

  Step 1: Webhook Configuration
  Set up the webhook URL in your external system (e.g., app, API) to send YouTube video IDs to this workflow.

  Step 2: API Access
  Ensure that your environment has access to YouTube’s public HTML content. An API key is not required since the workflow parses publicly available data.

  Step 3: AI Integration
  Verify the connection to the AI model used for report generation. Supported models include Google Gemini and OpenAI.
  Note that the system can be customized by modifying the provided prompt to suit specific analysis needs.

  Step 4: Testing
  Run a sample test with a YouTube video ID to ensure subtitles are correctly retrieved and the report is generated successfully.

  Expected Outcomes

  Effortless Content Analysis:** Generate thematic reports with minimal setup.
  No API Key Dependency:** Simplified access by leveraging YouTube’s public HTML.
  Actionable Insights:** Gain valuable information on video content for business, research, or personal projects.
  Cost Considerations:** The execution cost depends primarily on the model used and the length of the video (affecting token usage). Leveraging the free tier of Google Gemini models is recommended to minimize costs.
  Main Theme Extraction:** The default prompt excludes irrelevant promotional content, providing clear and focused thematic summaries.

  Estimated setup time: 15–20 minutes with a ready environment.

  Prompt Description

  The workflow includes a customizable prompt used to process subtitles in XML format and generate analytical reports. The generated report includes:

  Title: A brief phrase capturing the essence of the main theme.
  Body: An analytical description of the main theme, structured into a maximum of three concise paragraphs. It focuses on summarizing key ideas, recurring themes, and connections without referencing the source explicitly (e.g., avoiding phrases like “this video”). The system also removes content related to sales, subscriptions, or promotions to maintain a clear thematic focus.

  Example Output

  Title: The Ethical Challenges of Artificial Intelligence  

  Report:  
  Artificial intelligence presents significant challenges in areas such as privacy, fairness, and automated decision-making. Its implementation in critical sectors such as healthcare, justice, and security has sparked debates about inherent biases and lack of transparency. Additionally, there is growing concern over the ethical implications of automation, including its impact on employment and the global economy. Finally, the importance of establishing strong regulatory frameworks is highlighted to balance technological innovation with the protection of human rights.

nodes_used:
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name: Analyze Suspicious Email Contents with ChatGPT Vision
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-12-20T23:00:52.354Z
updated_at: 2024-12-20T23:00:52.354Z
url: https://n8n.io/workflows/2665
scraped_at: 2025-09-14 11:38:23
description:
  Phishing Email Detection and Reporting with n8n

  Who is this for?
  This workflow is designed for IT teams, security professionals, and managed service providers (MSPs) looking to automate the process of detecting, analyzing, and reporting phishing emails.

  What problem is this workflow solving?
  Phishing emails are a significant cybersecurity threat, and manually detecting and reporting them is time-consuming and prone to errors. This workflow streamlines the process by automating email analysis, generating detailed reports, and logging incidents in a centralized system like Jira.

  What this workflow does
  This workflow automates phishing email detection and reporting by integrating Gmail and Microsoft Outlook email triggers, analyzing the content and headers of incoming emails, and generating Jira tickets for flagged phishing emails. Here’s what happens:

  Email Triggers: Captures incoming emails from Gmail or Microsoft Outlook.
  Email Analysis: Extracts email content, headers, and metadata for analysis.
  HTML Screenshot: Converts the email’s HTML body into a visual screenshot.
  AI Phishing Detection: Leverages ChatGPT to analyze the email and detect potential phishing indicators.
  Jira Integration: Automatically creates a Jira ticket with detailed analysis and attaches the email screenshot for review by the security team.
  Customizable Reports: Includes options to customize ticket descriptions and adapt the workflow to organizational needs.

  Setup
  Authentication: Set up Gmail and Microsoft Outlook OAuth credentials in n8n to access your email accounts securely.
  API Keys: Add API credentials for the HTML screenshot service (hcti.io) and ChatGPT.
  Jira Integration: Configure your Jira project and issue types in the workflow.
  Workflow Configuration: Update sticky notes and nodes to include any additional setup or configuration details unique to your system.

  How to customize this workflow to your needs
  Email Filters**: Modify email triggers to filter specific subjects or sender addresses.
  Analysis Scope**: Adjust the ChatGPT prompt to refine phishing detection logic.
  Integration**: Replace Jira with your preferred ticketing system or modify the ticket fields to include additional information.

  This workflow provides an end-to-end automated solution for phishing email management, enhancing efficiency and reducing security risks. It’s perfect for teams looking to minimize manual effort and improve incident response times.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","n8n-nodes-base.microsoftOutlookTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.jira","n8n-nodes-base.code","n8n-nodes-base.jira","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Analyze & Sort Suspicious Email Contents with ChatGPT
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-12-21T00:48:36.281Z
updated_at: 2024-12-21T00:48:36.281Z
url: https://n8n.io/workflows/2666
scraped_at: 2025-09-14 11:38:22
description:
  Analyze & Sort Suspicious Email Contents with ChatGPT and Jira

  Who is this for?
  This workflow is tailored for IT security teams, managed service providers (MSPs), and organizations aiming to streamline the detection and reporting of phishing emails. It's especially useful for teams handling high email volumes and requiring quick, automated analysis.

  What problem is this workflow solving?
  Phishing emails pose a significant cybersecurity threat, and manual review processes are time-consuming and prone to human error. This workflow automates the identification of malicious emails, provides AI-driven insights, and generates structured reports, enabling faster and more efficient responses to email-based threats.

  What this workflow does
  This workflow integrates Gmail or Microsoft Outlook to monitor and capture incoming emails. It processes the email content and headers, converts the email's body to a visual screenshot for clarity, and uses ChatGPT's advanced AI to analyze the email for phishing indicators. Based on the analysis, it categorizes emails as potentially malicious or benign, creating detailed Jira tickets for each case. Attachments, including the email body and screenshots, are automatically uploaded for comprehensive reporting.

  Key steps include:
  Email Integration: Captures emails from Gmail or Microsoft Outlook.
  Content Processing: Extracts and organizes email content and metadata.
  AI Analysis: Uses ChatGPT to evaluate email content and headers.
  Classification: Categorizes emails as malicious or benign.
  Automated Reporting: Creates Jira tickets with detailed analysis and attachments.

  Setup
  Authentication: Configure Gmail or Microsoft Outlook credentials in n8n.
  API Keys: Add credentials for the HTML screenshot service (hcti.io) and OpenAI.
  Jira Configuration: Set up project and issue types in the Jira nodes.
  Customization: Update sticky notes and nodes to fit your organizational requirements, such as modifying the AI prompt or Jira ticket fields.

  How to customize this workflow to your needs
  Adjust email triggers to include or exclude specific senders or subjects.
  Refine the AI prompt in the ChatGPT node to tailor phishing detection criteria.
  Modify Jira ticket content to include additional fields or match specific workflows.

  This workflow is ideal for automating email threat detection, reducing response times, and enhancing overall cybersecurity processes. By leveraging AI-powered insights, it helps organizations stay ahead of phishing attacks.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","n8n-nodes-base.microsoftOutlookTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.jira","n8n-nodes-base.jira","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.jira","n8n-nodes-base.jira","n8n-nodes-base.convertToFile","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: 🛠️ Auto Workflow Positioning
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2024-12-21T21:35:21.612Z
updated_at: 2024-12-21T21:35:21.612Z
url: https://n8n.io/workflows/2667
scraped_at: 2025-09-14 11:38:23
description:
  Check Online Version !
  [https://n8n-tools.streamlit.app/](https://n8n-tools.streamlit.app/
  )
  Who is it for?  
  This workflow is perfect for n8n users who want to maintain clean and organized workflows without manually repositioning nodes.

  Whether you're building complex workflows or sharing them with a team, maintaining visual clarity is essential for efficiency and collaboration.

  This template automates the positioning process, saving time and ensuring consistent layout standards.

  How does it work?  
  The template is divided into two parts:  

  Positioning Engine:  
     A webhook node kicks off the process by receiving a workflow ID.  
     Using the provided workflow ID, an n8n API node fetches the workflow details.  
     The fetched workflow is sent to a processing webhook that calculates optimized positions for the nodes.  
     Finally, an n8n API node updates the workflow with the newly positioned nodes, ensuring a clean and professional layout.  

  Reusable Positioning Block:  
     This is an HTTP Request node that can be seamlessly integrated into any workflow you create. When triggered, it sends the current workflow for automatic positioning via the first part of this template.  

  How to set it up?  

  Enable n8n API Access:  
     Ensure that your n8n instance has API access enabled with the appropriate credentials.  

  Input Your n8n API URL and Credentials:  
     Open the template, locate the n8n API nodes, and update them with your instance API key.
     Update the URL of the 'Magic Positioning' Http Request node to point to your n8n instance webhook URL.

  Embed the Reusable Block:  
     Add the provided HTTP Request node to any of your workflows to instantly connect to the auto-positioning engine.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.retrieverVectorStore","@n8n/n8n-nodes-langchain.vectorStoreInMemory","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.chainRetrievalQa","n8n-nodes-base.switch","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.n8n","n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.n8n","n8n-nodes-base.respondToWebhook","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: 📚 Auto-generate documentation for n8n workflows with GPT and Docsify
author_name: Eduard
author_username: eduard
categories: []
created_at: 2024-12-22T21:51:40.651Z
updated_at: 2024-12-22T21:51:40.651Z
url: https://n8n.io/workflows/2669
scraped_at: 2025-09-14 11:38:22
description:
  This workflow creates a documentation system for n8n instances using Docsify.js. It serves a dynamic documentation website that allows users to:

  View an overview of all workflows in a tabular format
  Filter workflows by tags
  Access automatically generated documentation for each workflow
  Edit documentation with a live Markdown preview
  Visualize workflow structures using Mermaid.js diagrams

  &gt; 📺 Check out the short 2-min demonstration on LinkedIn. Don't forget to connect!

  🔧 Key Components

  Main Documentation Portal
  Serves a Docsify-powered website
  Provides a navigation sidebar with workflow tags
  Displays workflow status, creation date, and documentation links

  Documentation Generator
  Uses GPT model to auto-generate workflow descriptions
  Creates Mermaid.js diagrams of workflow structures
  Maintains consistent documentation format

  Live Editor
  Split-screen Markdown editor with preview
  Real-time Mermaid diagram rendering
  Save/Cancel functionality

  ⚙️ Technical Details

  Environment Setup
  Requires write access to the specified project directory
  Uses environment variables for n8n instance URL configuration
  Implements webhook endpoints for serving documentation

  ⚠️ Security Considerations
  &gt; Note: The current implementation doesn't include authentication for editing. Consider adding authentication for production use.

  Dependencies
  Docsify.js for documentation rendering
  Mermaid.js for workflow visualization
  OpenAI GPT for documentation generation

  🔍 Part of the n8n Observability Series

  This workflow is part of a broader series focused on n8n instance observability. Check out these related workflows:

  Workflow Dashboard - Get comprehensive analytics of your n8n instance
  Visualize Your n8n Workflows with Mermaid.js - Create beautiful workflow visualizations

  Each workflow in this series helps you better understand and manage your n8n automation ecosystem!

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.if","n8n-nodes-base.extractFromFile","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.sort","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.readWriteFile","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.readWriteFile","n8n-nodes-base.set","n8n-nodes-base.n8n","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.outputParserAutofixing","n8n-nodes-base.respondToWebhook","n8n-nodes-base.html","n8n-nodes-base.noOp","n8n-nodes-base.merge","n8n-nodes-base.html","n8n-nodes-base.executeCommand","n8n-nodes-base.merge","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.noOp","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.n8n","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.n8n","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.webhook"]

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name: LINE Assistant with Google Calendar and Gmail Integration
author_name: Boriwat Chanruang
author_username: boriwatopal
categories: []
created_at: 2024-12-23T06:49:51.118Z
updated_at: 2024-12-23T06:49:51.118Z
url: https://n8n.io/workflows/2671
scraped_at: 2025-09-14 11:38:23
description:
  Who is this for?
  This workflow is for small business owners, personal assistants, or project managers who rely on multiple platforms for communication and scheduling.
  Ideal for users managing customer support, personal scheduling, or group event coordination via LINE, Google Calendar, and Gmail.

  What problem is this workflow solving?
  Reduces the manual effort needed to manage conversations, schedule events, and handle email communications.
  Provides an intelligent system for replying to user messages and fetching relevant calendar or email information in real time.
  Bridges the gap between messaging platforms and productivity tools, improving efficiency.

  What this workflow does
  LINE Chatbot Automation**: Automatically processes and responds to messages received via LINE.
  Google Calendar Management**: Retrieves upcoming events or schedules new events dynamically based on user queries.
  Email Retrieval**: Fetches recent emails using Gmail and filters them based on user instructions.
  AI-Powered Replies**: Uses OpenAI GPT to interpret user queries and provide tailored responses.

  Setup
  Prerequisites:
     LINE Developer account and API access.
     Google Calendar and Gmail accounts with OAuth credentials.
     An n8n instance with access to environment variables.

  Steps:
     Set up environment variables (LINE_API_TOKEN and DYNAMIC_EMAIL).
     Configure API credentials for Google Calendar and Gmail in n8n.
     Test the workflow by sending a sample message via LINE.

  Enhancements:
     Use sticky notes to provide inline instructions for each node.
     Include a video walkthrough or a step-by-step document for first-time users.

  How to customize this workflow to your needs
  Localization**: Modify responses in the AI Agent node to match the language and tone of your audience.
  Integration**: Add more integrations like Slack or Microsoft Teams for additional notifications.
  Advanced Filters**: Add specific conditions to Gmail or Google Calendar nodes to fetch only relevant data, such as events with specific keywords or emails from certain senders.

  Advanced Use Cases
  Customer Support**: Automatically schedule meetings with clients based on their messages in LINE.
  Event Management**: Handle RSVP confirmations, event reminders, and email follow-ups for planned events.
  Personalized Assistant**: Use the workflow to act as a personal virtual assistant that syncs your schedule, replies to messages, and summarizes emails.

  Tips for Optimization
  Edit Fields Node**: Add a centralized node to configure dynamic inputs (e.g., tokens, emails, or thresholds) for easy updates.
  Fallback Responses**: Use a switch node to handle unrecognized input gracefully and provide clear feedback to users.
  Logs and Monitoring**: Add nodes to log interactions and track message flows for debugging or analytics.

  Let me know if you'd like me to expand on any specific section or add more customization ideas!

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolWikipedia","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.switch","n8n-nodes-base.webhook","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.gmailTool"]

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name: Create a Google Analytics Data Report with AI and sent it to E-Mail and Telegram
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2024-12-25T14:56:34.079Z
updated_at: 2024-12-25T14:56:34.079Z
url: https://n8n.io/workflows/2673
scraped_at: 2025-09-14 11:38:22
description:
  What this workflow does
  This workflow retrieves Google Analytics data from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary.
  The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account.

  This workflow has the following sequence:

  time trigger (e.g. every Monday at 7 a.m.)
  retrieval of Google Analytics data from the last 7 days
  assignment and summary of the data
  retrieval of Google Analytics data from the last 7 days of the previous year
  allocation and summary of the data
  preparation in tabular form and brief analysis by AI.
  sending the report as an email
  preparation in short form by AI for Telegram (optional)
  sending as Telegram message.

  Requirements
  The following accesses are required for the workflow:
  Google Analytics (via Google Analytics API): Documentation
  AI API access (e.g. via OpenAI, Anthropic, Google or Ollama)
  SMTP access data (for sending the mail)
  Telegram access data (optional for sending as Telegram message): Documentation

  Feel free to contact me via LinkedIn, if you have any questions!

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleAnalytics","n8n-nodes-base.emailSend","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.googleAnalytics","n8n-nodes-base.summarize","n8n-nodes-base.summarize","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi"]

workflow_json_compressed:
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name: Generate Stripe invoice and send it by email
author_name: Solomon
author_username: solomon
categories: []
created_at: 2024-12-26T23:01:02.981Z
updated_at: 2024-12-26T23:01:02.981Z
url: https://n8n.io/workflows/2675
scraped_at: 2025-09-14 11:38:24
description:
  Generating Stripe invoices through the API can be tricky since it requires four steps to generate and send it via email to the customer.

  With this workflow you can create Stripe invoices automatically and make Stripe send the invoices to the customer email.

  How it works

  To generate a Stripe invoice, you need to create a customer, specify the invoice items, create the invoice, and finalize it.

  What should be a simple task involves multiple steps.

  This workflow simplifies the process by providing everything pre-built for you.

  Who is this for?

  Anyone who wants to generate invoices automatically and send them to the customer’s email.

  Stripe will only send invoices to customers if you generate the invoice correctly through the API.
  .

  Check out my other templates

  👉 https://n8n.io/creators/solomon/

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

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name: Analyze Email Headers for IP Reputation and Spoofing Detection - Outlook
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-12-27T06:09:11.324Z
updated_at: 2024-12-27T06:09:11.324Z
url: https://n8n.io/workflows/2676
scraped_at: 2025-09-14 11:38:23
description:
  Analyze Emails for Security Insights

  Who is this for?

  This workflow is ideal for security teams, IT Ops professionals, and managed service providers (MSPs) responsible for monitoring and validating email traffic. It’s especially useful for organizations that need to identify potential phishing attempts, spam, or compromised accounts by analyzing email headers and IP reputation.

  What problem is this workflow solving?

  This workflow helps identify malicious or suspicious emails by verifying email authentication headers (SPF, DKIM, DMARC) and analyzing the reputation of the originating IP address. By automating these checks, it reduces manual analysis time and flags potential threats efficiently.

  What this workflow does

  Email Monitoring:** Polls a specified Microsoft Outlook folder for new emails in real-time.
  Header Analysis:** Retrieves and processes email headers to extract critical information such as authentication results and the sender’s IP address.
  IP Reputation Check:** Leverages external APIs (IP Quality Score and IP-API) to analyze the originating IP for potential spam or malicious activity.
  Authentication Validation:** Validates SPF, DKIM, and DMARC headers, determining if the email passes industry-standard authentication protocols.
  Data Aggregation and Reporting:** Combines all analyzed data into a unified format, ready for reporting or integration into downstream systems.
  Webhook Integration:** Outputs the findings via a webhook, enabling integration with alerting tools or security information and event management (SIEM) platforms.

  Setup

  Connect to Outlook:
     Configure the Microsoft Outlook trigger node with valid OAuth2 credentials.
     Specify the email folder to monitor for new messages.

  API Keys (Optional):
     Obtain an API key for IP Quality Score (https://ipqualityscore.com).
     Ensure the IP-API endpoint is accessible.
     This step is optional as ipqualityscore.com will provide a limited number of free lookups each month. See more details here.  

  Webhook Configuration:
     Set up a webhook endpoint to receive the output of the workflow.

  Optional Adjustments:
     Customize polling intervals in the trigger node.
     Modify header filters or extend the validation logic as needed.

  How to customize this workflow to your needs

  Add Alerts:** Use the Respond to Webhook node to trigger notifications in Slack, email, or any other communication channel.
  Integrate with SIEM:** Forward the workflow output to SIEM tools like Splunk or ELK Stack for further analysis.
  Modify Validation Rules:** Update SPF, DKIM, or DMARC logic in the Set nodes to align with your organization’s security policies.
  Expand IP Analysis:** Add more APIs or services to enrich IP reputation data, such as VirusTotal or AbuseIPDB.

  This workflow provides a robust foundation for email security monitoring and can be tailored to fit your organization's unique requirements. With its modular design and integration options, it’s a versatile tool to enhance your cybersecurity operations.

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name: Analyze Email Headers for IP Reputation and Spoofing Detection - Gmail
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-12-27T06:47:56.660Z
updated_at: 2024-12-27T06:47:56.660Z
url: https://n8n.io/workflows/2677
scraped_at: 2025-09-14 11:38:23
description:
  Analyze Emails for Security Insights

  Who is this for?

  This workflow is ideal for IT professionals, security analysts, and organizations looking to enhance their email security practices. It is particularly useful for those who need to analyze Gmail email headers for IP tracking, spoofing detection, and sender reputation assessment. 

  What problem is this workflow solving?

  Email spoofing and phishing attacks are significant cybersecurity threats. By analyzing email headers, this workflow provides detailed insights into the email's origin, authentication status, and the reputation of the sending IP address. It helps detect potential spoofing attempts and assess the trustworthiness of incoming emails.

  What this workflow does

  This n8n workflow automates the process of analyzing email headers received in Gmail. It performs the following key functions:

  Triggering and Email Header Extraction: It monitors Gmail inboxes for new emails and extracts their headers for analysis.
  Authentication Analysis: It validates SPF, DKIM, and DMARC authentication results to ensure the email adheres to industry-standard security protocols.
  IP Analysis: The workflow extracts the originating IP address and evaluates its reputation and geographic details using external APIs.
  Reputation Scoring: It integrates with IP Quality Score to detect spam activity and assess the sender's reputation.
  Consolidation and Webhook Response: All results are aggregated into a single JSON response, making it easy to integrate with third-party platforms or tools for further automation.

  Setup

  Authenticate Gmail: Configure the Gmail Trigger node with your Gmail account credentials.
  API Keys (Optional):
     Obtain an API key for IP Quality Score (https://ipqualityscore.com).
     Ensure the IP-API endpoint is accessible.
     This step is optional as ipqualityscore.com will provide a limited number of free lookups each month. See more details here.  
  Activate the Workflow: Ensure the workflow is active to process incoming emails in real-time.

  How to customize this workflow to your needs

  Add Alerts:** Use the Gmail - Respond to Webhook node to trigger notifications in Slack, email, or any other communication channel.
  Integrate with SIEM:** Forward the workflow output to SIEM tools like Splunk or ELK Stack for further analysis.
  Modify Validation Rules:** Update SPF, DKIM, or DMARC logic in the Set nodes to align with your organization’s security policies.
  Expand IP Analysis:** Add more APIs or services to enrich IP reputation data, such as VirusTotal or AbuseIPDB.

  This workflow provides a robust foundation for email security monitoring and can be tailored to fit your organization's unique requirements. With its modular design and integration options, it’s a versatile tool to enhance your cybersecurity operations.

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name: Export all Strava Activity Data to Google Sheets
author_name: Sherlockes
author_username: sherlockes
categories: []
created_at: 2024-12-28T22:23:06.727Z
updated_at: 2024-12-28T22:23:06.727Z
url: https://n8n.io/workflows/2678
scraped_at: 2025-09-14 11:38:24
description:
  What does this template help with?
  Save the data of activities recorded and stored in Strava to a Google Sheets document.

  How it works:
  We have a Google Sheets spreadsheet where each row represents a Strava activity with the date, reference, distance, time, and elevation.
  Periodically, the workflow checks the latest activities in our Strava account to see if any are missing from the spreadsheet and adds them to the list.
  All fields must be properly formatted according to how they are stored in the Google Sheets spreadsheet.

  Set up instructions
  Complete the Set up credentials step when you first open the workflow. You'll need a Google Sheets and Strava account.
  In the 'activities' node, you must enter the name of the file and the sheet where you want to save the imported data.
  In the 'Strava' node, you must select the corresponding credential.
  You can adjust the format of dates, times, and distances according to your needs in the 'strava_last' node.

  The rest of the information is available at  sherblog.es

  Template was created in n8n v1.72.1

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.strava","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.removeDuplicates","n8n-nodes-base.googleSheets","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.set","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.sort"]

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name: ⚡AI-Powered YouTube Video Summarization & Analysis
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2024-12-29T03:01:45.635Z
updated_at: 2024-12-29T03:01:45.635Z
url: https://n8n.io/workflows/2679
scraped_at: 2025-09-14 11:38:20
description:
  -- Disclaimer: This workflow uses a community node and therefore only works for self-hosted n8n users --



  Transform YouTube videos into comprehensive summaries and structured analysis instantly. This n8n workflow automatically extracts, processes, and analyzes video transcripts to deliver clear, organized insights without watching the entire video.

  Time-Saving Features
  🚀 Instant Processing
  Simply provide a YouTube URL and receive a structured summary within seconds, eliminating the need to watch lengthy videos. Perfect for research, learning, or content analysis.

  🤖 AI-Powered Analysis
  Leverages GPT-4o-mini to analyze video transcripts, organizing key concepts and insights into a clear, hierarchical structure with main topics and essential points.

  Smart Processing Pipeline
  📝 Automated Transcript Extraction
  Supports public YouTube video
  Handles multiple URL formats
  Extracts complete video transcripts automatically

  🧠 Intelligent Content Organization
  Breaks down content into main topics
  Highlights key concepts and terminology
  Maintains technical accuracy while improving clarity
  Structures information logically with markdown formatting

  Perfect For
  📚 Researchers & Students
  Quick comprehension of educational content and lectures without watching entire videos.

  💼 Business Professionals
  Efficient analysis of industry talks, presentations, and training materials.

  🎯 Content Creators
  Rapid research and competitive analysis of video content in your niche.

  Technical Implementation
  🔄 Workflow Components
  Webhook endpoint for URL submission
  YouTube API integration for video details
  Transcript extraction system
  GPT-4 powered analysis engine
  Telegram notification system (optional)

  Transform your video content consumption with an intelligent system that delivers structured, comprehensive summaries while saving hours of viewing time.

nodes_used:
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name: AI-Powered Social Media Amplifier
author_name: Mudit Juneja
author_username: beingmudit
categories: []
created_at: 2024-12-29T21:28:30.325Z
updated_at: 2024-12-29T21:28:30.325Z
url: https://n8n.io/workflows/2681
scraped_at: 2025-09-14 11:38:21
description:
  &gt; Reach out to me  for any setup help/consulting.


  Automate the curation and sharing of trending GitHub discussions from Hacker News to Twitter and LinkedIn. This workflow leverages AI to generate engaging posts, streamlining your social media content creation and distribution.

  How it Works

  Crawl Hacker News for GitHub Posts: The workflow fetches trending GitHub-related discussions from Hacker News.
  Extract Key Information: Relevant data such as post titles, URLs, and metadata are extracted and filtered to focus only on unposted content.
  Fetch Additional Details: For each GitHub post, the workflow retrieves extra information from the GitHub repository page to enrich the post content.
  Generate Social Media Posts: Using AI, the workflow automatically generates tailored posts for Twitter and LinkedIn based on the collected data.
  Post to Twitter & LinkedIn: The generated content is posted to your Twitter and LinkedIn accounts.
  Track and Log Posts: Each post is logged in Airtable for tracking, and its status is updated to ensure no duplicate posts are made.
  Telegram Notification: After posting, a summary of the posts is sent to your Telegram chat for real-time updates.

  Requirements

  n8n Account**: Set up and configured. Sign up here.
  API Credentials**: Valid keys for LinkedIn, Twitter, Airtable, OpenAI, and Telegram.
  Airtable Base**: Configured with fields such as Title, URL, Post Content, Status, and Timestamp. Get started with Airtable.
  Telegram Chat ID**: For receiving real-time notifications.


  Set Up Steps

  Clone the Workflow: Import the workflow into your n8n environment using the provided JSON.
  Configure API Credentials: Enter your API keys for LinkedIn, Twitter, Airtable, OpenAI, and Telegram into the respective nodes.
  Set Up Airtable Base: Create an Airtable base with fields such as Title, URL, Post Content, Status, and Timestamp.
  Customize Telegram Chat ID: Modify the 'Ping me' node with your Telegram chat ID to receive notifications.
  Run the Workflow: Activate the workflow to start the automated content curation and posting process.

  Additional Resources

  n8n AI Agentic Workflows Guide
  n8n AI Workflow Tutorial
  n8n Community Tutorial on Building an AI-Powered Telegram Bot


  &gt; Note: Chat GPT prompt should/can be tweaked in the step to give the desired behaviour.

  Sample Posts from my X and LinkedIn -** 

nodes_used:
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