# templates

name: Automated End-to-End Fine-Tuning of OpenAI Models with Google Drive Integration
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-23T14:01:48.509Z
updated_at: 2025-01-23T14:01:48.509Z
url: https://n8n.io/workflows/2781
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  1. How it Works  
  This n8n workflow automates fine-tuning OpenAI models through these key steps:  
  Manual Trigger**:  
    Starts with the "When clicking ‘Test workflow’" event to initiate the process.  
    Downloads a .jsonl file from Google Drive   
  Upload to OpenAI**:  
    Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune").  
  Create Fine-tuning Job**:  
    Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with:  
          {  
        "training_file": "{{ $json.id }}",  
        "model": "gpt-4o-mini-2024-07-18"  
      }  
        OpenAI automatically starts training the model based on the provided file.  
  Interaction with the Trained Model**:  
    An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages.  

  2. Set up Steps  
  To configure the workflow:  
  Prepare the Training File:  
     Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples).  
     Upload it to Google Drive and update the ID in the "Google Drive" node.  

  Configure Credentials:  
     Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api).  
     OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes.  

  Customize the Model:  
     In the "OpenAI Chat Model" node, specify the name of your fine-tuned model (e.g., ft:gpt-4o-mini-...).  
     Update the HTTP request body (Create Fine-tuning Job) if needed (e.g., a different base model).  

  Start the Workflow:  
     Use the manual trigger ("Test workflow") to begin the upload and training process.  
     Test the model via the "Chat Trigger" (chat messages).  

  Integrated Documentation:  
     Follow the instructions in the Sticky Notes to:  
       Properly format the .jsonl (Step 1).  
       Monitor progress on OpenAI (Step 2, link: https://platform.openai.com/finetune/).  

  Note: Ensure the .jsonl file adheres to OpenAI’s required structure and that credentials are valid.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","machine learning","data processing"],"complexity":"medium","use_cases":["Automate end-to-end fine-tuning of OpenAI models","Convert training data stored in Google Drive (.jsonl) into fine-tune jobs","Rapidly test and iterate custom model behavior via chat","Embed fine-tuning into deployment or CI workflows"],"estimated_setup_minutes":20,"required_services":["Google Drive (OAuth2)","OpenAI API (file upload & fine-tuning endpoints)"],"key_features":["Downloads training .jsonl from Google Drive","Uploads training file to OpenAI with purpose 'fine-tune'","Creates fine-tuning job via OpenAI API POST","Allows interacting with the resulting fine-tuned model via chat","Manual trigger for controlled runs and built-in notes for setup"],"target_audience":["Developers","Machine learning engineers","Data scientists/ML practitioners"]}


name: Build an OpenAI Assistant with Google Drive Integration
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-23T14:21:30.480Z
updated_at: 2025-01-23T14:21:30.480Z
url: https://n8n.io/workflows/2782
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  Workflow Overview  
  This workflow automates the creation and management of a custom OpenAI Assistant for a travel agency ("Travel with us"), leveraging Google Drive for document storage.

  How It Works  

  1. Create the OpenAI Assistant  
  Node**: OpenAI  
    Action: Creates a custom assistant named "Travel with us" Assistant using the gpt-4o-mini model.  
    Instructions:  
      Respond only using the provided document (e.g., agency-specific info).  
      Stay friendly, brief, and focused on travel-related queries.  
      Ignore irrelevant questions politely.  
    Credentials: Requires OpenAI API key.  

  2. Upload Agency Document  
  Google Drive Node**:  
    Action: Downloads a Google Doc as a PDF.   
  OpenAI2 Node**:  
    Action: Uploads the PDF to OpenAI with purpose: "assistants".  
    Output: Generates a file_id.  

  3. Update the Assistant with the Document  
  OpenAI Node**:  
    Action: Updates the assistant to include the uploaded file.

  4. Chat Interaction  
  Chat Trigger**:  
    Activates when a message is received ("When chat message received").  
  OpenAI Assistant Node**:  
    Action: Uses the updated assistant to respond to user queries.  
    Memory: Window Buffer Memory retains chat context for coherent conversations.  

  Set Up Steps  
  Prepare the Document:  
     Store your travel agency guide in Google Drive (e.g., as a Google Doc).  
     Update the Google Drive node with your document’s ID.  

  Configure Credentials:  
     Google Drive: Connect via OAuth2 (googleDriveOAuth2Api).  
     OpenAI: Add your API key to all OpenAI nodes.  

  Customize the Assistant:  
     Modify the instructions in the OpenAI node to reflect your agency’s needs.  
     Ensure the document includes FAQs, policies, and travel info.  

  Test the Workflow:  
     Trigger manually ("Test workflow") to create the assistant and upload the file.  
     Send a chat message (e.g., "What are your travel packages?") to test responses.  

  Dependencies  
  Google Drive Account**: To store and retrieve the agency document.  
  OpenAI API Access**: For assistant creation and file uploads.  

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi"]

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metadata_json:
  {"categories":["automation","integration","knowledge management","customer support","ai assistant"],"complexity":"medium","use_cases":["Create a custom AI assistant for a travel agency","Ingest agency documents into an assistant knowledge base","Provide contextual chat responses using uploaded documents","Automate document retrieval from Google Drive and upload to OpenAI","Maintain conversational context with window buffer memory"],"estimated_setup_minutes":25,"required_services":["OpenAI API (API key, assistants & file upload access)","Google Drive (OAuth2 access to the agency document)"],"key_features":["Create and configure a custom OpenAI assistant (gpt-4o-mini)","Download Google Doc as PDF and upload to OpenAI with purpose 'assistants'","Update assistant to include uploaded document (file_id)","Chat trigger to handle incoming messages","Window buffer memory to retain conversational context"],"target_audience":["travel agencies","customer support teams","developers implementing AI chatbots for businesses"]}


name: AI marketing report (Google Analytics & Ads, Meta Ads), sent via email/Telegram
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2025-01-23T14:40:20.555Z
updated_at: 2025-01-23T14:40:20.555Z
url: https://n8n.io/workflows/2783
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  What this workflow does
  This workflow retrieves Online Marketing data (Google Analytics for several domains, Google Ads, Meta Ads) 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 Online Marketing data from the last 7 days (via sub workflows)
  assignment and summary of the data
  retrieval of Online Marketing data from the same time period 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
  Google Ads (via HTTP Request -&gt; Google Ads API):Documentation
  Meta Ads (via Facebook Graph 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

  You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools.
  You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g. “Analytics_Domain_5).

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

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

workflow_json_compressed:
  [encoded data, 13224 characters]

metadata_json:
  {"categories":["automation","marketing analytics","integrations","reporting"],"complexity":"complex","use_cases":["Weekly marketing performance reporting","Cross-channel ad performance comparison (Google Ads, Meta Ads)","Year-over-year trend analysis","Automated executive summaries and alerts","Sending condensed updates via Telegram"],"estimated_setup_minutes":180,"required_services":["Google Analytics API","Google Ads API (via HTTP)","Meta Ads / Facebook Graph API","AI API (OpenAI, Anthropic, Google, Ollama, etc.)","SMTP (for email delivery)","Telegram Bot API (optional)"],"key_features":["Scheduled weekly trigger","Pulls last 7 days and same period previous year","Supports multiple domains via sub-workflows","AI prepares tabular data, analyzes and generates summaries","Sends full report by email and shortened version to Telegram (optional)"],"target_audience":["Digital marketers","Growth teams","Marketing analysts / BI analysts at agencies or in-house teams\",\"Developers/automation engineers setting up reporting pipelines"]}


name: Personal Shopper Chatbot for WooCommerce with RAG using Google Drive and openAI
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-23T14:55:58.691Z
updated_at: 2025-01-23T14:55:58.691Z
url: https://n8n.io/workflows/2784
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios:  
  Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce.  
  General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive.  

  How It Works  

  1. Chat Interaction & Intent Detection  
  Chat Trigger**:  
    Starts when a user sends a message ("When chat message received").  
  Information Extractor**:  
    Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question.  
    Extracts:  
      search (true/false).  
      keyword, priceRange, SKU, category (if product-related).  
    Example:  
          {  
        "search": true,  
        "keyword": "red handbags",  
        "priceRange": { "min": 50, "max": 100 },  
        "SKU": "BAG123",  
        "category": "women's accessories"  
      }  
  2. Product Search (WooCommerce Integration)  
  AI Agent**:  
    If search: true, routes the request to the personal_shopper tool.  
    WooCommerce Node:  
      Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU).  
      Filters products in stock (stockStatus: "instock").  
      Returns matching products (e.g., "red handbags under €100").  

  3. General Inquiries (RAG System)  
  RAG Tool**:  
    If search: false, uses the Qdrant Vector Store to retrieve store information from documents.  
    Google Drive Integration:  
      Documents (e.g., store policies, FAQs) are stored in Google Drive.  
      Downloaded, split into chunks, and embedded into Qdrant for semantic search.  
    OpenAI Chat Model: Generates answers based on retrieved documents (e.g., "Our store opens at 9 AM").  

  Set Up Steps  

  1. Configure the RAG System  
  Google Drive Setup**:  
    Upload store documents .  
    Update the Google Drive2 node with your folder ID.  
  Qdrant Vector Database**:  
    Clean the collection (update Qdrant Vector Store node with your URL).  
    Use Embeddings OpenAI to convert documents into vectors.  

  2. Configure OpenAI & WooCommerce  
  OpenAI Credentials**:  
    Add your API key to all OpenAI nodes (OpenAI Chat Model, Embeddings OpenAI, etc.).  
  WooCommerce Integration**:  
    Connect your WooCommerce store (credentials in the personal_shopper node).  
    Ensure product data is synced and accessible.  

  3. Customize the AI Agent  
  Intent Detection**:  
    Modify the Information Extractor’s system prompt to align with your store’s terminology.  
  RAG Responses**:  
    Update the tool description to reflect your store’s documents.  

  Notes  
  This template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support.

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

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.wooCommerceTool","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent"]

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metadata_json:
  {"categories":["e-commerce","integration","conversational AI","knowledge retrieval (RAG)"],"complexity":"complex","use_cases":["Product discovery via chat (keyword, SKU, price filters)","Automated answers to store FAQs and policies","Hybrid assistant combining catalog search + knowledge base","Personalized shopping suggestions based on user intent"],"estimated_setup_minutes":60,"required_services":["OpenAI (Chat + Embeddings)","WooCommerce (store API)","Google Drive (documents storage)","Qdrant (vector store)","n8n (workflow runtime)"],"key_features":["Intent extraction (search vs general question) using OpenAI","Product queries to WooCommerce with filters (keyword, SKU, price range, in-stock)","RAG pipeline: download Google Drive docs → embed → Qdrant retrieval","OpenAI chat model composes answers from retrieved documents","Routing/agent logic to choose product search vs RAG response','Configurable system prompts and tool descriptions"],"target_audience":["e-commerce store owners and managers","Developers/automation engineers implementing chat assistants","Customer support teams wanting automated FAQ responses"]}


name: RSS Feed News Processing and Distribution Workflow
author_name: PollupAI
author_username: zeerobug
categories: []
created_at: 2025-01-23T16:39:37.327Z
updated_at: 2025-01-23T16:39:37.327Z
url: https://n8n.io/workflows/2785
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Who is this for?

  This workflow is designed for professionals and teams who need to monitor multiple RSS feeds, filter the latest content, and distribute actionable updates as a Trello comment. Ideal for content managers, marketers, and team leads managing news or content pipelines.

  What problem is this workflow solving?

  Manually monitoring RSS feeds and keeping track of the latest content can be time-consuming. This workflow automates the aggregation, filtering, and distribution of news, ensuring that only relevant and timely updates are shared with your team or audience.

  What this workflow does:
  Aggregates RSS Feeds: Pulls data from up to three RSS feeds simultaneously.
  Filters Content: Filters articles based on their publication date (default: last 7 days).
  Organizes and Sorts: Sorts filtered articles by date for clarity.
  Formats Updates: Transforms news items into Markdown format for better readability.
  Publishes and Notifies: Posts comments to Trello cards and sends an email to a moderator to check the comment.

  Setup:
  Connect your RSS feeds by configuring the RSS Read nodes.
  Link your Trello and Gmail accounts for seamless integration.
  Adjust the schedule trigger to set how often the workflow should run (e.g., daily, weekly).
  Test the workflow to ensure all connections and configurations are correct.

  How to customize this workflow to your needs:
  Change the Number of RSS Feeds: Add or remove RSS Read nodes and update the merge configuration accordingly.
  Adjust the Date Filter: Modify the date logic in the “Filter by date” node to include more or fewer days.
  Limit the Number of Articles: Adjust the limit in the “Limit news to x” node.
  Custom Formatting: Update the Transform node to format the news items differently.
  Alternative Notifications: Replace Trello and Gmail with other integrations, such as Slack or Microsoft Teams.

  This workflow ensures your team stays informed with minimal effort and delivers content updates in an organized and professional manner.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.rssFeedRead","n8n-nodes-base.set","n8n-nodes-base.filter","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.code","n8n-nodes-base.trello","n8n-nodes-base.gmail","n8n-nodes-base.merge","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","content management","notifications","monitoring"],"complexity":"medium","use_cases":["Monitor multiple RSS feeds for new content","Aggregate and filter recent articles","Curate and format news updates into Markdown","Post updates as Trello comments for team visibility","Send moderator notification emails for review"],"estimated_setup_minutes":20,"required_services":["RSS feeds (any RSS URLs)","Trello account","Gmail (or other email service configured in n8n)"],"key_features":["Aggregates up to three RSS feeds concurrently","Filters articles by publication date (default: last 7 days)","Sorts and limits articles for clarity","Transforms items into Markdown","Posts comments to Trello cards and sends an email notification to a moderator via Communication/Gmail node"],"target_audience":["Content managers","Marketers","Team leads and project managers coordinating content/news pipelines"]}


name: Create a Branded AI-Powered Website Chatbot
author_name: Wayne Simpson
author_username: nocodecreative
categories: []
created_at: 2025-01-23T17:44:16.860Z
updated_at: 2025-01-23T17:44:16.860Z
url: https://n8n.io/workflows/2786
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 19:29:29
description:
  Create a Branded AI Website Chatbot

  Engage website visitors with an intelligent chat widget powered by OpenAI. This template includes:

  💬 Natural conversation handling
  📅 Microsoft Outlook calendar integration
  📝 Lead capture and information gathering
  🔄 Human handoff capabilities

  Simply add a JavaScript snippet to your website and configure the workflow to match your needs. Follow our detailed setup guide to get started in minutes.



  &gt; Note: Widget includes a "Powered By" affiliate link

nodes_used:
  ["@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.respondToWebhook","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","n8n-nodes-base.respondToWebhook","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: Backup n8n Workflows to Bitbucket
author_name: Gareth B. Davies
author_username: garethbdavies
categories: []
created_at: 2025-01-24T00:25:33.966Z
updated_at: 2025-01-24T00:25:33.966Z
url: https://n8n.io/workflows/2787
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  An automated backup solution designed for self-hosted n8n users to automatically backup their workflows to Bitbucket, leveraging Bitbucket's free private repository offering.

  Perfect for maintaining version control of your n8n workflows without additional costs.

  How it works:
  Runs on a regular schedule to check all workflows in your n8n instance
  Compares each workflow with its version in Bitbucket
  Only uploads workflows that are new or have changed
  Uses basic rate limiting to stay within Bitbucket's API limits
  Formats filenames for easy tracking and includes timestamps in commit messages
  Handles errors gracefully with automatic retries

  Set up steps (10-15 minutes):
  Create a free Bitbucket account and private repository
  Create a Bitbucket App Password with repository write access
  Add Bitbucket credentials to n8n (using your username and app password)
  Set up n8n API access (generate API key in your n8n instance)
  Configure your Bitbucket workspace and repository names in the Set node
  Optional: Adjust the backup schedule (default: 2 AM daily)

  Perfect for n8n self-hosters who want:
  Version control for their workflows
  Automated daily backups
  Free private repository storage
  Easy workflow recovery
  Change tracking over time

  The workflow includes basic error handling and rate limiting to ensure reliable backups even with larger numbers of workflows. Adjust your timing based on https://support.atlassian.com/bitbucket-cloud/docs/api-request-limits/.

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.n8n","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","backup","integration","version control","devops"],"complexity":"medium","use_cases":["Automated daily backup of n8n workflows","Maintain version control and change history for workflows","Quick recovery of workflows after accidental deletion or corruption","Auditing and tracking workflow changes over time","Migrating or syncing workflows between instances"],"estimated_setup_minutes":15,"required_services":["Bitbucket Cloud (repository & API access)","Self‑hosted n8n instance with API access"],"key_features":["Scheduled backups","Differential upload — only new or changed workflows","Basic rate limiting to respect Bitbucket API limits","Timestamped commit messages and formatted filenames","Automatic retries and basic error handling"],"target_audience":["n8n self-hosters","Developers and automation engineers","Sysadmins and DevOps practitioners"]}


name: Daily meetings summarization with Gemini AI
author_name: Johnny Rafael
author_username: johnnyrafael
categories: []
created_at: 2025-01-24T03:54:33.086Z
updated_at: 2025-01-24T03:54:33.086Z
url: https://n8n.io/workflows/2789
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow implements the Gemini AI chat model to summarize your daily meetings and send the summary to a Slack channel daily at 9 AM (or any other time you choose).

  It automatically retrieves your Google Calendar events and feeds them to the model.

  The workflow uses Google’s Gemini AI for response generation.

  How it works
  The workflow uses a Scheduled Trigger Node as the main trigger.
  The AI Agent Node uses the Google Calendar action to retrieve relevant meeting data.
  The AI Agent sends the retrieved information to the Google Gemini Chat Model (gemini-flash).
  The Google Gemini Chat Model generates a summary and informative response based on today’s meetings.

  ++Setup Steps++
  Google Cloud Project and Vertex AI API:

  	Create a Google Cloud project.
  	Enable the Vertex AI API for your project.

  Google AI API Key:
  	Obtain a Google AI API key from Google AI Studio.
  Credentials in n8n:
  	Configure credentials in your n8n environment for:
  		Google Gemini (PaLM) API (using your Google AI API key).
  Import the Workflow:
  	Import this workflow into your n8n instance.
  Configure the Workflow:
  	Update both Slack and Gemini nodes with your credentials.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.googleCalendarTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini"]

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metadata_json:
  {"categories":["automation","ai","productivity","calendar","communication"],"complexity":"medium","use_cases":["Daily meeting summaries","Automated team updates to Slack","Extracting action items and key points from meetings","Reducing meeting recap overhead","Centralizing meeting context for teams"],"estimated_setup_minutes":30,"required_services":["Google Cloud Project with Vertex AI (Google Gemini / Google AI Studio)","Google Calendar","Slack","n8n instance with configured credentials"],"key_features":["Scheduled trigger to run daily at a set time","Automatically retrieves today’s Google Calendar events","Uses Google Gemini (gemini-flash) to generate concise summaries","Posts generated summaries to a Slack channel","Configurable nodes and schedule for customization"],"target_audience":["team leads","project managers","productivity-focused professionals and admins who run recurring meetings"]}


name: AI Fitness Coach Strava Data Analysis and Personalized Training Insights
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-01-24T09:02:16.267Z
updated_at: 2025-01-24T09:02:16.267Z
url: https://n8n.io/workflows/2790
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Detailed Title
  "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n"

  Description
  This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp.

  Workflow Details

  Trigger: Strava Activity Updates
  Node:** Strava Trigger
  Purpose:** Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more.
  Integration:** Uses Strava API for real-time synchronization.

  Step 1: Data Preprocessing
  Node:** Code
  Purpose:** Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes.
  Logic:** A recursive function flattens JSON input to create a clean and readable structure.

  Step 2: AI Analysis with Google Gemini
  Node:** Google Gemini Chat Model
  Purpose:** Leverages Google Gemini's advanced language model to analyze the activity data. 
  Functionality:**
    Identifies key performance metrics.
    Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling).
    Offers tailored recommendations and motivational advice.

  Step 3: Generate Structured Output
  Node:** Structure Output
  Purpose:** Processes the AI-generated response to create a structured format, such as headings, paragraphs, and bullet lists.
  Output:** Formats the response for clear communication.

  Step 4: Convert to HTML
  Node:** Convert to HTML
  Purpose:** Converts the structured output into an HTML format suitable for email or other presentation methods.
  Output:** Ensures the response is visually appealing and easy to understand.

  Step 5: Send Email with Training Insights
  Node:** Send Email
  Purpose:** Sends a detailed email to the athlete with performance insights, training recommendations, and motivational messages.
  Integration:** Utilizes Gmail or SMTP for secure and efficient email delivery.

  Optional Step: WhatsApp Notifications
  Node:** WhatsApp Business Cloud
  Purpose:** Sends a summary of the activity analysis and key recommendations via WhatsApp for instant access.
  Integration:** Connects to WhatsApp Business Cloud for automated messaging.

  Additional Notes
  Customization:  
     You can modify the AI prompt to adapt the recommendations to the athlete's specific goals or fitness levels.
     The workflow is flexible and can accommodate additional nodes for more advanced analysis or output formats.

  Scalability:  
     Ideal for individual athletes or coaches managing multiple athletes.  
     Can be expanded to include additional metrics or insights based on user preferences.

  Performance Metrics Handled:  
     Swimming: SWOLF, stroke count, pace.  
     Cycling: Cadence, power zones, elevation.  
     Running: Pacing, stride length, heart rate zones.

  Implementation Steps
  Set Up Strava API Key:
     Log in to Strava Developers to generate your API key.
     Integrate the API key into the Strava Trigger node.

  Configure Google Gemini Integration:
     Use your Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node.

  Customize Email and WhatsApp Messaging:
     Update the Send Email and WhatsApp Business Cloud nodes with the recipient’s details.

  Automate Execution:
     Deploy the workflow and use n8n's scheduling features or cron jobs for periodic execution.

  GET n8n Now  

  N8N COURSE  
  n8n Book  

  Developer Notes
  Author:** Amjid Ali  
  improvements.
  Resources:**
  See in Action: Syncbricks Youtube  
    PayPal: Support the Developer  
    Courses : SyncBricks LMS

  By using this workflow, triathletes and coaches can elevate training to the next level with AI-powered insights and actionable recommendations.

nodes_used:
  ["n8n-nodes-base.stravaTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.gmail","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.emailSend","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp"]

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metadata_json:
  {"categories":["integration","automation","data processing","AI","sports & fitness"],"complexity":"complex","use_cases":["Personalized triathlon training insights","Automated activity analysis from Strava","Coach-to-athlete communication and notifications","Performance trend monitoring and feedback","Motivational messaging and recommendations"],"estimated_setup_minutes":60,"required_services":["Strava API","Google Gemini (PaLM) API","Gmail or SMTP service","WhatsApp Business Cloud API","n8n platform"],"key_features":["Real-time Strava activity trigger","Recursive JSON flattening and data preprocessing","AI-driven analysis using Google Gemini Chat","Activity-specific insights for swimming, cycling, and running (SWOLF, cadence, power, pace, heart rate zones)","Structured output formatting and HTML conversion for clear emails"],"target_audience":["Triathletes","Triathlon coaches and personal trainers","Sports scientists and performance analysts with basic automation skills」「Fitness app developers integrating coaching workflows"]}


name: Scrape Trustpilot Reviews with DeepSeek, Analyze Sentiment with OpenAI
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-24T14:51:16.882Z
updated_at: 2025-01-24T14:51:16.882Z
url: https://n8n.io/workflows/2792
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Workflow Overview  
  This workflow automates the process of scraping Trustpilot reviews, extracting key details, analyzing sentiment, and saving the results to Google Sheets. It uses OpenAI for sentiment analysis and HTML parsing for review extraction.

  How It Works  

  1. Scrape Trustpilot Reviews   
  HTTP Request**:  
    Fetches review pages from Trustpilot (https://it.trustpilot.com/review/{{company_id}}).  
    Paginates through pages (up to max_page limit).  
  HTML Parsing**:  
    Extracts review URLs using CSS selectors
    Splits the URLs into individual review links.  

  2. Extract Review Details   
  Information Extractor**:  
    Uses DeepSeek to extract structured data from the review:  
      Author: Name of the reviewer.  
      Rating: Numeric rating (1-5).  
      Date: Review date in YYYY-MM-DD format.  
      Title: Review title.  
      Text: Full review text.  
      Total Reviews: Number of reviews by the user.  
      Country: Reviewer’s country (2-letter code).  

  3. Sentiment Analysis  
  Sentiment Analysis Node**:  
    Uses OpenAI to classify the review text as Positive, Neutral, or Negative.  
    Example output:  
          {  
        "category": "Positive",  
        "confidence": 0.95  
      }  
  4. Save to Google Sheets  
  Google Sheets Node**:  
    Appends or updates the extracted data to a Google Sheet 

  Set Up Steps  

  1. Configure Trustpilot Scraping  
  Edit Fields1 Node**:  
    Set company_id to the Trustpilot company name 
    Set max_page to limit the number of pages scraped.  

  2. Configure Google Sheets  
  Google Sheets Node**:  
    Update the documentId with your Google Sheet ID 
    Ensure the sheet has the required columns (Id, Data, Nome, etc.).  

  3. Configure OpenAI  
  OpenAI Chat Model Node**:  
    Add your OpenAI API key.  
  Sentiment Analysis Node**:  
    Ensure the categories match your desired sentiment labels (Positive, Neutral, Negative).  

  Key Components  
  Nodes**:  
    HTTP Request/HTML: Scrape and parse Trustpilot reviews.  
    Information Extractor: Extract structured review data using DeepSeek.  
    Sentiment Analysis: Classify review sentiment.  
    Google Sheets: Save and update review data.  
  Credentials**:  
    OpenAI API key. 
    DeepSeek API key.  
    Google Sheets OAuth2.  

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

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metadata_json:
  {"categories":["automation","integration","data processing","web-scraping"],"complexity":"medium","use_cases":["Customer review monitoring","Sentiment analysis for customer feedback","Reputation management","Product/feature insight extraction","Competitor review tracking"],"estimated_setup_minutes":20,"required_services":["Trustpilot (web scraping of review pages)","DeepSeek API (information extraction)","OpenAI API (sentiment analysis)","Google Sheets (OAuth2)"],"key_features":["Paginated scraping of Trustpilot review pages","HTML parsing to extract review URLs","DeepSeek-driven structured extraction (author, rating, date, title, text, total reviews, country)","OpenAI sentiment classification with confidence scores","Append/update results to Google Sheets (dedupe/update capability)"],"target_audience":["Customer success teams","Product managers","Marketing teams/brand managers`,`Data analysts`,`Developers implementing integrations"]}


name: Workflow Results to Markdown Notes in Your Obsidian Vault, via Google Drive
author_name: Obsidi8n
author_username: derruttie
categories: []
created_at: 2025-01-24T21:00:40.332Z
updated_at: 2025-01-24T21:00:40.332Z
url: https://n8n.io/workflows/2794
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow converts any n8n workflow outputs into Markdown notes that are accessible in your Obsidian Vault through Google Drive synchronization.

  Setup Requirements
  Create a designated folder in Google Drive (Desktop).
  Create a symbolic link between this folder and a new target folder in your Obsidian Vault.
  Configure Google Drive n8n node settings.
  Send the output of any workflow to the trigger, and the notes will appear in your Vault folder.
  	
  Optional Features
  You can use AI agents to:
  Write notes in your preferred format (e.g., Zettelkasten).
  Compose YAML front matter.
  Suggest tags.

  Use Cases
  Convert RSS feed items to notes.
  Create notes from YouTube video transcripts.
  Transform tasks in Slack messages into Obsidian tasks.
  (Requires setting up a corresponding workflow, e.g., RSS trigger, YouTube transcriber, or Slack bot.)

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

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metadata_json:
  {"categories":["automation","integration","note-taking","AI-assisted"],"complexity":"medium","use_cases":["Convert any n8n workflow output into an Obsidian Markdown note","Import RSS feed items as individual Obsidian notes","Save YouTube video transcripts into Obsidian","Turn Slack messages or tasks into Obsidian tasks/notes"],"estimated_setup_minutes":20,"required_services":["n8n","Google Drive (Desktop sync)","Obsidian (local vault accessible via file system)","OpenAI or other AI agent provider (optional, for AI formatting/front matter/tags)","YouTube API / transcription service (optional, for video transcripts)","Slack API (optional, for Slack-to-Obsidian workflows)"],"key_features":["Transforms arbitrary workflow outputs into Markdown files","Writes notes directly into a Google Drive folder that syncs with an Obsidian vault","Optional AI agents to format content (Zettelkasten, YAML front matter, tag suggestions)","Reusable node set usable with multiple triggers (RSS, YouTube, Slack, etc.)","Configurable note templates and structured output parsing"],"target_audience":["Obsidian users","Knowledge workers and researchers","Content creators (podcasts, video, blog workflows)"]}


name: Programmatically Retrieve Embeddable Getty Images
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-01-24T21:51:37.266Z
updated_at: 2025-01-24T21:51:37.266Z
url: https://n8n.io/workflows/2795
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  How it works:
  This workflow empowers marketers and content creators to seamlessly include Getty Images in their posts, provided they adhere to Getty Images' guidelines:

  Enter a search phrase and select criteria for Getty Images' editorial collection.
  Retrieve the latest images that match your input parameters.
  Extract embeddable links for use in your content.
  Automate this previously manual process to save time and maintain compliance.

  Set up steps:
  Estimated time: ~10 minutes.
  Configure the input node with your desired search terms and criteria.
  Link the workflow to your preferred CMS or storage solution.
  Perform a test run to ensure proper retrieval and embedding of images.
  Review Getty Images' usage guidelines for compliance before publishing.

  This solution is ideal for independent news channels and small publications looking for a legal, automated way to use Getty Images on their websites without incurring costs.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.html","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stopAndError","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest"]

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metadata_json:
  {"categories":["integration","content management","media retrieval","automation"],"complexity":"medium","use_cases":["Automatically search Getty Images editorial collection","Extract embeddable image links for publishing","Integrate embeddable images into a CMS or storage solution","Save time on manual image sourcing while maintaining compliance"],"estimated_setup_minutes":10,"required_services":["Getty Images embeddable images API (or Getty Images HTTP endpoints)","CMS or storage service (e.g., WordPress, headless CMS, cloud storage)"],"key_features":["Search Getty Images editorial collection by phrase and criteria","Retrieve latest matching images","Extract and output embeddable links/HTML","Webhook/HTTP integration for CMS or downstream automation","Automated, repeatable workflow with manual test/run support"],"target_audience":["marketers","content creators","independent news channels and small publications looking to embed Getty content legally (editorial use)"]}


name: Voiceflow Demo Support Chatbot
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-01-24T22:53:18.722Z
updated_at: 2025-01-24T22:53:18.722Z
url: https://n8n.io/workflows/2796
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Submission Overview for Voiceflow Demo Workflow

  View the YouTube video for this workflow here.

  Who is this for?
  This workflow is ideal for businesses and developers using Voiceflow to power AI voice chatbots. It benefits teams that want to enhance chatbot functionality through integrations with platforms like Zendesk, Google Calendar, and Airtable.

  What problem is this workflow solving?
  The workflow addresses the need for seamless integration of chatbot interactions with backend systems. It automates customer service tasks such as ticket creation, meeting scheduling, and data reporting, reducing manual effort and enhancing efficiency.

  What does this workflow do?
  Customer Lookup:** Checks the database for existing customers and returns relevant details or a "NOT_FOUND" status.
  Zendesk Ticket Creation:** Automates the creation of support tickets for customer issues.
  Meeting Scheduling:** Integrates with Google Calendar to provide availability and schedule meetings.
  Transcript Reporting:** Aggregates interaction data and sends it to Airtable for analysis by the product team.

  Setup
  Configure your Voiceflow chatbot to connect to this workflow via a webhook.
  Set up the required integrations:
     Zendesk API: For ticket creation.
     Google Calendar API: For scheduling.
     Airtable API: For storing transcripts.
  Customize the workflow's nodes to match your use case, such as database fields or API endpoints.
  Deploy the workflow on your n8n instance and test the integrations.

  How to customize this workflow to your needs
  Adjust database queries to match your customer data schema.
  Modify the Zendesk ticket payload to include additional fields or custom formats.
  Update Google Calendar configurations for different scheduling requirements.
  Add or remove Airtable fields based on the product team's analysis needs.

  This template adheres to n8n’s submission guidelines, ensuring clarity, relevance, and broad applicability for users in customer service, product development, and automation.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","customer_service","scheduling","reporting"],"complexity":"medium","use_cases":["Automate support ticket creation from chatbot interactions","Schedule meetings via chatbot using Google Calendar","Lookup customer records and return status to Voiceflow","Aggregate and store conversation transcripts for product analytics","Enrich chatbot workflows with AI-driven logic and conditional flows"],"estimated_setup_minutes":60,"required_services":["Voiceflow (webhook)","Zendesk API","Google Calendar API","Airtable API","Google Sheets (optional)","AI provider (e.g., OpenAI)"],"key_features":["Customer lookup and NOT_FOUND handling","Automated Zendesk ticket creation","Google Calendar availability checks and meeting scheduling","Transcript aggregation and storage in Airtable","Webhook integration with Voiceflow for real-time chatbot flows and responses"],"target_audience":["Developers building Voiceflow chatbots","Customer support teams automating ticketing workflows","Product teams needing conversation analytics and transcripts stored centrally (Airtable)\",\"Automation/operations engineers integrating backend systems"]}


name: Notion to Pinecone Vector Store Integration
author_name: Udit Rawat
author_username: ailistmaster
categories: []
created_at: 2025-01-25T01:29:58.675Z
updated_at: 2025-01-25T01:29:58.675Z
url: https://n8n.io/workflows/2797
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n automation is designed to extract, process, and store content from Notion pages into a Pinecone vector store. Here's a breakdown of the workflow:

  Notion - Page Added Trigger: The automation starts by monitoring for newly added pages in a specific Notion database. It triggers whenever a new page is created, capturing the page's metadata.

  Notion - Retrieve Page Content: Once triggered, the automation fetches the full content of the newly added Notion page, including blocks like text, images, and videos.

  Filter Non-Text Content: The next step filters out non-text content (such as images and videos), ensuring only textual content is processed.

  Summarize - Concatenate Notion's blocks content: The remaining text content is concatenated into a single block of text for easier processing.

  Token Splitter: The concatenated text is then split into manageable tokens, which are chunks of text that can be used for embedding.

  Create metadata and load content: Metadata such as the page ID, creation time, and title are added to the content, making it easy to reference and track.

  Embeddings Google Gemini: The processed text is passed through a Google Gemini model to generate embeddings, which are numerical representations of the text that capture its semantic meaning.

  Pinecone Vector Store: Finally, the embeddings, along with the content and metadata, are stored in a Pinecone vector store, making it searchable and ready for use in applications like document retrieval or natural language processing tasks.

  This workflow ensures that every new page added to the Notion database is processed into a format that can be easily searched and used in machine learning applications. The automation runs every minute to capture new data in real-time, providing an up-to-date and searchable vector database of Notion content.

  Use Case:

  This automation converts Notion pages into vector embeddings and stores them in Pinecone for enhanced search and AI-driven insights. It’s ideal for teams using Notion for knowledge management, enabling semantic search and context-based content retrieval. For example, employees can easily find relevant information across documents, and data scientists can use AI models to analyze and summarize the content stored in Notion.

nodes_used:
  ["@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.notionTrigger","n8n-nodes-base.notion","n8n-nodes-base.filter","n8n-nodes-base.summarize","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.vectorStorePinecone"]

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metadata_json:
  {"categories":["integration","data processing","automation","vector search"],"complexity":"medium","use_cases":["Semantic search over Notion content","Knowledge base indexing","Document retrieval for assistants or chatbots","AI-driven summarization and analytics","Keeping vector DB in sync with Notion in near real-time"],"estimated_setup_minutes":45,"required_services":["Notion API","Pinecone","Google Gemini (embeddings) / Vertex AI or PaLM API","n8n"],"key_features":["Triggers on new Notion pages and fetches full content","Filters non-text blocks and concatenates text","Token/chunk splitting for embedding-friendly segments","Generates embeddings via Google Gemini","Stores embeddings with metadata in Pinecone vector store for search/retrieval"],"target_audience":["Developers","Data scientists / ML engineers","Knowledge managers / documentation teams」「Product managers / AI product builders"]}


name: Create Threads on Bluesky
author_name: Gareth B. Davies
author_username: garethbdavies
categories: []
created_at: 2025-01-25T10:01:11.632Z
updated_at: 2025-01-25T10:01:11.632Z
url: https://n8n.io/workflows/2798
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Create engaging, structured threads on Bluesky with precise control over post timing and visibility.

  This workflow helps content creators and social media managers schedule and publish threaded posts that maintain proper connections and formatting, ensuring your content appears exactly as intended.

  How it works
  Creates an initial visible post that starts your thread
  Adds a series of hidden reply posts that form the body of your thread
  Maintains proper parent-child relationships between posts to ensure correct threading
  Enforces timing delays between posts to prevent rate limiting
  Concludes with two visible posts at the end of your thread

  The result is a clean, professional-looking thread where only the first and last two posts are immediately visible to your followers, encouraging engagement while maintaining a clean profile view.

  Set up steps (10-30 minutes)
  Create a Bluesky account
  Enter your Bluesky handle and app password in the "Set Bluesky Credentials" node
  Customize the post text in the Code nodes to match your content:
     Initial visible post
     Hidden reply posts
     Final visible posts
  Adjust the scheduling in the "Run Daily at 9 AM" node to match your preferred posting time

  Suggested enhancements
  Add error handling with retry logic for API failures
  Add input validation for post length and credential format
  Include error notifications via email or Slack
  Add data persistence to track successful posts and resume failed threads
  Make timing delays configurable with exponential backoff
  Add monitoring for rate limits and API quotas

  For Social Media Managers who want:
  Control over post visibility and timing
  Automated posting of long-form content
  Professional-looking content presentation

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

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metadata_json:
  {"categories":["social media automation","integration","scheduling"],"complexity":"medium","use_cases":["Schedule and publish threaded posts on Bluesky","Publish long-form content split into hidden replies with controlled visibility","Maintain correct parent-child post relationships for threading","Avoid rate limiting with enforced delays between posts","Automate daily or recurring thread publishing"],"estimated_setup_minutes":20,"required_services":["Bluesky (AT Protocol) account and app password / API access","n8n instance (self-hosted or cloud) for workflow execution"],"key_features":["Creates initial visible post and multiple hidden reply posts to form a thread","Maintains proper parent-child relationships so threading appears correctly","Enforces timing delays between posts to prevent rate limits","Ends thread with two visible posts to improve profile appearance","Scheduling node to run at a specified time (e.g., daily at 9 AM) for automated posting runs"],"target_audience":["Social media managers","Content creators","Community managers (BlueSky communities / profiles maintainers)"]}


name: AI-Powered Information Monitoring with OpenAI, Google Sheets, Jina AI and Slack
author_name: Dataki
author_username: dataki
categories: []
created_at: 2025-01-25T11:55:56.526Z
updated_at: 2025-01-25T11:55:56.526Z
url: https://n8n.io/workflows/2799
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  Check Legal Regulations:
  This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry!

  📌 Purpose  
  This workflow enables automated and AI-driven topic monitoring, delivering concise article summaries directly to a Slack channel in a structured and easy-to-read format.  
  It allows users to stay informed on specific topics of interest effortlessly, without manually checking multiple sources, ensuring a time-efficient and focused monitoring experience.  

  To get started, copy the Google Sheets template required for this workflow from here.  

  🎯 Target Audience  
  This workflow is designed for:  
  Industry professionals** looking to track key developments in their field.  
  Research teams** who need up-to-date insights on specific topics.  
  Companies** aiming to keep their teams informed with relevant content.  

  ⚙️ How It Works  
  Trigger: A Scheduler initiates the workflow at regular intervals (default: every hour).  
  Data Retrieval:  
     RSS feeds are fetched using the RSS Read node.  
     Previously monitored articles are checked in Google Sheets to avoid duplicates.  
  Content Processing:  
     The article relevance is assessed using OpenAI (GPT-4o-mini).  
     Relevant articles are scraped using Jina AI to extract content.  
     Summaries are generated and formatted for Slack.  
  Output:  
     Summaries are posted to the specified Slack channel.  
     Article metadata is stored in Google Sheets for tracking.  

  🛠️ Key APIs and Nodes Used  
  Scheduler Node:** Triggers the workflow periodically.  
  RSS Read:** Fetches the latest articles from defined RSS feeds.  
  Google Sheets:** Stores monitored articles and manages feed URLs.  
  OpenAI API (GPT-4o-mini):** Classifies article relevance and generates summaries.  
  Jina AI API:** Extracts the full content of relevant articles.  
  Slack API:** Posts formatted messages to Slack channels.  

  This workflow provides an efficient and intelligent way to stay informed about your topics of interest, directly within Slack.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","monitoring","AI","content curation"],"complexity":"complex","use_cases":["Automated topic/news monitoring","Team updates and alerts via Slack","Research and literature tracking","Content curation and summary generation","Competitive intelligence and trend watching"],"estimated_setup_minutes":45,"required_services":["Google Sheets","OpenAI (GPT-4o-mini)","Jina AI","Slack","RSS feeds (source websites)","n8n (Scheduler node)"],"key_features":["Periodic RSS fetching and monitoring","Duplicate checking and record-keeping in Google Sheets","Relevance classification using OpenAI","Full-article extraction with Jina AI","AI-generated concise summaries formatted for Slack posts"],"target_audience":["Industry professionals","Research teams","Companies and internal communications teams (e.g., product/marketing/ops)"]}


name: Zoom AI Meeting Assistant creates mail summary, ClickUp tasks and follow-up call
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2025-01-25T17:30:31.754Z
updated_at: 2025-01-25T17:30:31.754Z
url: https://n8n.io/workflows/2800
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Update 19-04-2025
  Change from OpenAI to Claude 3.7 Sonnet module
  Adding the Think Tool

  The update enables significantly better results to be achieved. This is particularly noticeable during longer meetings!

  What this workflow does
  This workflow retrieves the Zoom meeting data from the last 24 hours. The transcript of the last meeting is then retrieved, processed, a summary is created using AI and sent to all participants by email.
  AI is then used to create tasks and follow-up appointments based on the content of the meeting.

  Important: You need a Zoom Workspace Pro account and must have activated Cloud Recording/Transcripts!

  This workflow has the following sequence:

  manual trigger (Can be replaced by a scheduled trigger or a webhook)
  retrieval of of Zoom meeting data
  filter the events of the last 24 hours
  retrieval of transcripts and extract of the text
  creating a meeting summary, format to html and send per mail
  create tasks and follow-up call (if discussed in the meeting) in ClickUp/Outlook (can be replaced by Gmail, Airtable, and so forth) via sub workflow

  Requirements:
  Zoom Workspace (via API and HTTP Request): Documentation
  Microsoft Outlook: Documentation
  ClickUp: Documentation
  AI API access (e.g. via OpenAI, Anthropic, Google or Ollama)
  SMTP access data (for sending the mail)

  You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools.
  You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g. “Analytics_Domain_5).

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

nodes_used:
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metadata_json:
  {"categories":["automation","integration","meeting productivity","AI-assisted workflows"],"complexity":"complex","use_cases":["Automated meeting transcription retrieval and processing","AI-generated meeting summaries emailed to participants","Automatic creation of tasks in ClickUp from meeting items","Scheduling follow-up calls in Outlook (or alternatives)","Modular sub-workflows for extendable integrations"],"estimated_setup_minutes":90,"required_services":["Zoom Workspace Pro (Cloud Recording / Transcripts)","ClickUp","Microsoft Outlook (or Gmail alternative)","AI API (e.g., Anthropic Claude 3.7 Sonnet, OpenAI, Google, Ollama)","SMTP access (or configured mail service)","n8n (with ability to run sub-workflows and HTTP requests)"],"key_features":["Fetches Zoom meetings from last 24 hours and extracts transcripts","Uses AI (Claude 3.7 Sonnet / Think Tool) to create concise summaries","Formats summaries to HTML and sends email to all participants","Generates ClickUp tasks and schedules follow-up calls in Outlook","Modular design using separate sub-workflows (Execute workflow trigger nodes)"],"target_audience":["Team leads and managers","Operations and project managers","Developers / automation engineers implementing integrations into n8n workflows"]}


name: Automatically Import your Meta Threads Posts into Notion
author_name: Geekaz / Kazen
author_username: geekaz
categories: []
created_at: 2025-01-26T13:04:30.670Z
updated_at: 2025-01-26T13:04:30.670Z
url: https://n8n.io/workflows/2802
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this for?
  This template is designed for social media managers, content creators, data analysts, and anyone who wants to automatically save and analyze their Meta Threads posts in Notion.

  It’s particularly useful for:

  Building a personal archive of your Threads content.
  Training AI models using your social media data.
  Tracking your online presence and engagement.

  What this workflow does
  This workflow uses the Meta Threads API to automatically retrieve your posts and import them into a Notion database.

  It retrieves the post content, date, and time, and stores them in designated properties within your Notion database.

  Setup
  Get Threads Access Token and ID: Obtain a long-lived access token and your Threads ID from the Meta Threads developer platform. This token auto-refreshes, ensuring uninterrupted workflow operation.

  Configure Credentials and Date Range: In the “Set Credentials” node (using edit fields), enter your token and ID. 

  Set the since and until parameters in the “Set Date Range” node to specify the post import period.

  Connect to Notion and Create a Database: Connect to your Notion workspace and create a database with these properties (customize with the “Create Properties” node):

  a. Title: Threads post URL (Notion entry title).

  b. Threads ID: Unique post ID (prevents duplicate imports).

  c. Username: Post author (for future multi-account/source management).

  d. Post Date: Original post date.

  e. Source (Multi-Select): “Threads” tag (for future multi-platform import and filtering).

  f. Created: Import date and time.

  g. Import Check (Optional): For use with a separate post-categorization workflow.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","social media","data archiving","content management"],"complexity":"medium","use_cases":["Build a personal archive of Meta Threads posts","Feed social media data into AI training or analytics workflows","Track online presence and engagement over time","Centralize multi-account or multi-platform content in Notion"],"estimated_setup_minutes":30,"required_services":["Meta Threads API (Meta developer platform) — access token and Threads ID","Notion API / Notion workspace","n8n instance (to run the workflow)"],"key_features":["Automatically fetches Threads posts and imports them into a Notion database","Maps post content, post date/time, URL/title, and author to Notion properties","Date-range filtering (since/until) for selective imports","Duplicate prevention using unique Threads ID property","Scheduled runs and batch processing for scalable imports (splitInBatches, schedule nodes)"],"target_audience":["social media managers","content creators","data analysts/researchers"]}


name: Generate Instagram Content from Top Trends with AI Image Generation
author_name: mustafa kendigüzel
author_username: mustafakendiguzel
categories: []
created_at: 2025-01-26T14:36:11.785Z
updated_at: 2025-01-26T14:36:11.785Z
url: https://n8n.io/workflows/2803
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 19:31:35
description:
  How it works

  This automated workflow discovers trending Instagram posts and creates similar AI-generated content. Here's the high-level process:

  1. Content Discovery & Analysis
  Scrapes trending posts from specific hashtags
  Analyzes visual elements using AI
  Filters out videos and duplicates

  2. AI Content Generation
  Creates unique images based on trending content
  Generates engaging captions with relevant hashtags
  Maintains brand consistency while being original

  3. Automated Publishing
  Posts content directly to Instagram
  Monitors publication status
  Sends notifications via Telegram

  Set up steps

  Setting up this workflow takes approximately 15-20 minutes:

  1. API Configuration (7-10 minutes)
  Instagram Business Account setup
  Telegram Bot creation
  API key generation (OpenAI, Replicate, Rapid Api)

  2. Database Setup (3-5 minutes)
  Create required database table
  Configure PostgreSQL credentials

  3. Workflow Configuration (5-7 minutes)
  Set scheduling preferences
  Configure notification settings
  Test connection and permissions

  Detailed technical specifications and configurations are available in sticky notes within the workflow.

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.splitInBatches","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","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.postgres","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.postgres","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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  [encoded data, 9660 characters]

metadata_json:
  {"categories":["automation","social media","content creation"],"complexity":"medium","use_cases":["Automatically discover trending topics and turn them into Instagram post ideas","Generate AI images for social content based on top trends","Schedule and batch-create Instagram-ready posts","Preview or notify teammates via Telegram before posting","Filter and refine generated content before publishing"],"estimated_setup_minutes":45,"required_services":["Google Trends or other trend source (Twitter/Reddit)","AI image generation API (e.g., OpenAI DALL·E, Stability, Midjourney API)","Instagram / Facebook Graph API for posting","Telegram (for previews/notifications)","n8n instance with credentials configured"],"key_features":["Scheduled polling of top trends","Automatic prompt creation from trending topics","Batch processing of multiple ideas","AI image generation for each idea","Conditional filters to refine or skip results (If nodes)"],"target_audience":["social media managers","content creators and influencers","digital marketers and growth teams around Instagram campaigns"]}


name: Analyse papers from Hugging Face with AI and store them in Notion
author_name: AI Native
author_username: ainative
categories: []
created_at: 2025-01-26T15:19:36.239Z
updated_at: 2025-01-26T15:19:36.239Z
url: https://n8n.io/workflows/2804
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow automates the process of retrieving Hugging Face paper summaries, analyzing them with OpenAI, and storing the results in Notion. Here’s a breakdown of how it works:

  ⏰ Scheduled Trigger: 
  The flow is set to run automatically at 8 AM on weekdays.
  📄 Fetching Paper Data: 
  It fetches Hugging Face paper summaries using their API.
  🔍 Data Check: 
  Before processing, the workflow checks if the paper already exists in Notion to avoid duplicates.
  🤖 Content Analysis with OpenAI: 
  If the paper is new, it extracts the summary and uses OpenAI to analyze the content.
  📥 Store Results in Notion: 
  After analysis, the summarized data is saved in Notion for easy reference.

  ⚙️ Set Up Steps for Automation

  Follow these steps to set up this automated workflow with Hugging Face, OpenAI, and Notion integration:

  🔑 Obtain API Tokens: 
  You’ll need the Notion and OpenAI API tokens to authenticate and connect these services with n8n.
  🔗 Integration in n8n: 
  Link Hugging Face, OpenAI, and Notion by configuring the appropriate nodes in n8n.
  🔧 Configure Workflow Logic:
     Set up a cron trigger for automatic execution at 8 AM on weekdays.
     Use an HTTP request node to fetch Hugging Face paper data.
     Add logic to check if the data already exists in Notion.
     Set up the OpenAI integration to analyze the paper’s summary.
     Store the results in Notion for easy access and reference.

  Result:

   

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.notion","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.notion","n8n-nodes-base.html"]

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metadata_json:
  {"categories":["automation","integration","data-processing","knowledge-management","ai"],"complexity":"medium","use_cases":["Automatically ingest new Hugging Face paper summaries","Generate AI-powered analyses and summaries of papers","Populate a searchable Notion knowledge base","Avoid duplicate entries when syncing research content","Automate literature monitoring for teams"],"estimated_setup_minutes":20,"required_services":["Hugging Face API","OpenAI API","Notion API","n8n"],"key_features":["Scheduled weekday trigger (8 AM)","Fetch paper summaries via Hugging Face API","Duplicate check against Notion before processing","OpenAI-powered content analysis and summarization","Save analyzed results into Notion for reference"],"target_audience":["researchers","data scientists / ML engineers","knowledge workers and technical product managers who track papers"]}


name: Todoist weekly email of completed tasks
author_name: Martijn Smit
author_username: smitmartijn
categories: []
created_at: 2025-01-26T16:27:59.627Z
updated_at: 2025-01-26T16:27:59.627Z
url: https://n8n.io/workflows/2805
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  This workflow template helps Todoist users get a weekly overview of their completed tasks via email, making it easier to review their past week.

  Why use this workflow?
  Todoist doesn’t provide completed task reports or filters in its built-in reports or n8n app. This workflow solves that by using Todoist’s public API to fetch your completed tasks.

  How it works
  Runs every Friday afternoon (or manually).
  Uses the Todoist public API to retrieve completed tasks.
  Excludes specific projects you set (e.g., a grocery list).
  Sends an email summary, grouping tasks by the day they were completed.

  Set up steps
  Copy your Todoist API token (found here).
  Create a Todoist API credential in n8n.
  Create an SMTP credential in n8n.
     Alternatively, use a preferred email service like Brevo, Mailjet, etc.
  Import this workflow template.
  In the Get completed tasks via Todoist API step, select your Todoist API credential.
  In the Send Email step:
     Select your SMTP credential.
     Set the sender and recipient email addresses.
  Run the workflow manually and check your inbox!

  Ignoring specific projects
  If you do not want your grocery list, workouts, or other tasks from specific Todoist projects showing up in your weekly summary, modify the step called Optional: Ignore specific projects and change this line:

  const ignoredProjects = ['2335544024'];

  This should be an array with the id of each project you'd like to ignore. You can find a list of your projects (inc. their Ids) by visiting this link: https://api.todoist.com/rest/v2/projects 

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

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","reporting","notifications","productivity"],"complexity":"simple","use_cases":["Weekly personal productivity review","Team/manager weekly status summary","Automated task-completion retrospectives","Tracking habits or recurring tasks completion"],"estimated_setup_minutes":10,"required_services":["Todoist (public API and API token)","SMTP or email provider (e.g., Brevo, Mailjet) for sending email","n8n instance"],"key_features":["Scheduled weekly run (Friday) or manual trigger","Fetch completed tasks via Todoist public API","Exclude specific Todoist projects by ID","Group completed tasks by day of completion","Send formatted email summary"],"target_audience":["Todoist users","Productivity enthusiasts","Individual contributors and managers wanting weekly summaries of completed work using n8n"]}


name: Create QuickBooks Online Customers With Sales Receipts For New Stripe Payments
author_name: Artur
author_username: arlusm1
categories: []
created_at: 2025-01-27T13:06:22.575Z
updated_at: 2025-01-27T13:06:22.575Z
url: https://n8n.io/workflows/2807
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Streamline your accounting by automatically creating QuickBooks Online customers and sales receipts whenever a successful Stripe payment is processed. Ideal for businesses looking to reduce manual data entry and improve accounting efficiency.

  How it works

  Trigger: The workflow is triggered when a new successful payment intent event is received from Stripe.
  Retrieve Customer Data: Fetches customer details from Stripe associated with the payment.
  Check QuickBooks Customer: Searches QuickBooks Online to see if the customer already exists using their email address.
  Create or Use Existing Customer: If the customer doesn't exist in QuickBooks, they are created; otherwise, the existing customer is used.
  Generate Sales Receipt: A sales receipt is created in QuickBooks Online with payment details, including item descriptions, amounts, and currency.

  Set up steps

  Connect Accounts: Authenticate both your QuickBooks Online and Stripe accounts in n8n.
  Webhook Setup: Configure the Stripe webhook to send payment_intent.succeeded events to this workflow.
  Test the Workflow: Trigger a test payment in Stripe to validate the integration.
  Customize Details: Adjust item descriptions or other fields in the QuickBooks sales receipt JSON body as needed.

  This workflow requires basic familiarity with n8n, but setup can be completed in under 15 minutes for most users.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stripe","n8n-nodes-base.stripeTrigger","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.quickbooks","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.httpRequest"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","accounting"],"complexity":"simple","use_cases":["Automatically create QuickBooks Online customers from Stripe payments","Generate sales receipts in QuickBooks for received payments","Reduce manual data entry for bookkeeping","Keep payment and accounting records in sync for ecommerce or services"],"estimated_setup_minutes":15,"required_services":["QuickBooks Online","Stripe","n8n (cloud or self-hosted)"],"key_features":["Triggers on Stripe payment_intent.succeeded webhook events","Fetches Stripe customer details and maps to QuickBooks customer","Searches QuickBooks by email and creates customer if missing","Creates sales receipts in QuickBooks with item, amount and currency","Configurable sales receipt JSON payload and item descriptions"],"target_audience":["Small business owners","Accountants and bookkeepers","Ecommerce merchants using Stripe and QuickBooks Online"]}


name: Enrich Linkedin Profiles from Google Sheets via RapidAPI
author_name: PollupAI
author_username: zeerobug
categories: []
created_at: 2025-01-27T14:52:02.869Z
updated_at: 2025-01-27T14:52:02.869Z
url: https://n8n.io/workflows/2808
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  LinkedIn Profile Enrichment Workflow

  Who is this for?

  This workflow is ideal for recruiters, sales professionals, and marketing teams who need to enrich LinkedIn profiles with additional data for lead generation, talent sourcing, or market research.

  What problem is this workflow solving?

  Manually gathering detailed LinkedIn profile information can be time-consuming and prone to errors. This workflow automates the process of enriching profile data from LinkedIn, saving time and ensuring accuracy.

  What this workflow does

  Input: Reads LinkedIn profile URLs from a Google Sheet.
  Validation: Filters out already enriched profiles to avoid redundant processing.
  Data Enrichment: Uses RapidAPI's Fresh LinkedIn Profile Data API to retrieve detailed profile information.
  Output: Updates the Google Sheet with enriched profile data, appending new information efficiently.

  Setup

  Google Sheet: Create a sheet with a column named linkedin_url and populate it with the profile URLs to enrich.
  RapidAPI Account: Sign up at RapidAPI and subscribe to the Fresh LinkedIn Profile Data API.
  API Integration: Replace the x-rapidapi-key and x-rapidapi-host values with your credentials from RapidAPI.
  Run the Workflow: Trigger the workflow and monitor the updates to your Google Sheet.

  How to customize this workflow

  Filter Criteria**: Modify the filter step to include additional conditions for processing profiles.
  API Configuration**: Adjust API parameters to retrieve specific fields or extend usage.
  Output Format**: Customize how the enriched data is appended to the Google Sheet (e.g., format, column mappings).
  Error Handling**: Add steps to handle API rate limits or missing data for smoother automation.

  This workflow streamlines LinkedIn profile enrichment, making it faster and more effective for data-driven decision-making.

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

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metadata_json:
  {"categories":["automation","data enrichment","integration"],"complexity":"medium","use_cases":["Lead generation","Talent sourcing / recruiting","Market research","Data enrichment for CRM or outreach","Automated profile data collection"],"estimated_setup_minutes":20,"required_services":["Google Sheets","RapidAPI (Fresh LinkedIn Profile Data API)"],"key_features":["Reads LinkedIn URLs from Google Sheets","Filters out already-enriched profiles to avoid duplicates","Calls RapidAPI Fresh LinkedIn Profile Data API to enrich profiles","Writes/appends enriched data back to Google Sheet","Configurable filter, API parameters, and output mapping"],"target_audience":["Recruiters","Sales professionals / SDRs","Marketing teams / growth marketers」「Data analysts / ops teams"]}


name: Microsoft Outlook AI Email Assistant with contact support from Monday and Airtable
author_name: Cognitive Creators
author_username: cognitivecreators
categories: []
created_at: 2025-01-28T05:04:49.156Z
updated_at: 2025-01-28T05:04:49.156Z
url: https://n8n.io/workflows/2809
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
description:
  Microsoft Outlook AI Email Assistant

  Prerequisites

  1. Microsoft 365 Login Credentials
  Provide your Office 365 credentials to connect Outlook.

  2. Monday.com
  Generate an API token and have a board with your contact details.

  3. Airtable
  Obtain an API key (or personal access token) and set up a base to store:
    Contacts (populated by the Monday.com sync).
    Rules & Categories (used by the AI Email Assistant).
  Use this Airtable base as the template: Airtable AI Email Assistant Template. Define your own rules, categories, and delete rules.

  4. OpenAI API Key
  Sign up for OpenAI if you don’t already have an account.
  Generate a new API key at OpenAI API Keys.

  What the System Does

  1. Daily Contact Sync (Monday.com → Airtable)
  Runs once a day to pull the latest contacts from Monday.com and store or update them in Airtable.

  2. AI Email Categorisation & Prioritisation
  Fetches Outlook emails with filters.
  Cleans and processes email content.
  Matches emails with known contacts from Airtable.
  Uses an AI agent to classify, categorise, and prioritise emails.
  Updates Outlook categories and importance based on AI results.
  Runs in parallel with Airtable rules & categories retrieval for real-time decision-making.

  Workflow 1: Daily Contact Sync (Monday.com → Airtable)

  Purpose
  Keep Airtable’s Contacts table up to date by pulling new or updated contact data from Monday.com daily.

  Steps
  Schedule Trigger  
     Runs at a set interval (daily) to initiate contact syncing.

  Monday.com: Get Contacts  
     Reads the specified board/columns from Monday.com where you store contact details.

  Airtable - Contacts  
     Upserts (adds or updates) the fetched Monday.com data into Airtable’s Contacts table.
     Ensures daily updates reflect changes from Monday.com.

  Result
  A consolidated contact list in Airtable, ready for AI email categorisation.

  Workflow 2: Categorise & Prioritise Outlook Emails

  Purpose
  Fetches Outlook emails, cleans and processes their content, matches senders with known contacts, and uses AI to categorise and prioritise them.

  Steps

  1. Get Outlook Emails with Filters
  Trigger**: Either scheduled (Check Mail Schedule Trigger) or manual (Test Workflow).
  Outlook Filters**:
    Not flagged (flag/flagStatus == 'notFlagged').
    Not categorised (not categories/any()).

  🔹 Result: A batch of fresh, unprocessed emails ready for processing.

  2. Sanitise Email
  Convert to Markdown: Strips **HTML tags and normalises formatting.
  Email Messages Processing: Allows manual removal of **signatures, disclaimers, or extra content.

  🔹 Result: A clean, AI-friendly email for categorisation.

  3. Match Contact
  Loop Over Emails**: Iterates over each email.
  Contact Lookup: Checks Airtable’s **Contacts table (updated daily).
  Merge Data: Enriches emails with known **client, supplier, or internal team info.

  🔹 Result: Enhanced email context for AI processing.

  4. AI Agent to Categorise & Prioritise
  Retrieve Rules & Categories**  
    Reads Rules, Categories, and Delete Rules from Airtable in parallel.
  AI: Analyse Email (Tools Agent)**  
    Uses email text, sender info, and rules to build a structured AI prompt.
  OpenAI Chat Model**  
    Processes the AI prompt and outputs:
      Category
      Subcategory (optional)
      Priority level
      Short rationale
  Structured Output Parser**  
    Ensures AI response is valid JSON format.

  🔹 Result:  
  Each email is labelled, categorised, and prioritised with AI-driven logic.

  5. Set Outlook Category & Importance
  Set Category: Updates Outlook with the assigned **category.
  Check Priority Conditions** (If Node):  
    If Action Required or from a VIP, mark as High Priority.
  Set Importance: Updates the email's **importance flag in Outlook.

  🔹 Result:  
  Outlook is updated with categories & importance based on AI recommendations.

  Parallel Processing: Retrieve Rules & Categories
  Runs alongside the email categorisation workflow.
  Ensures Airtable-based rules are available before AI processing.

  Steps
  Airtable: Get Rules & Categories  
     Fetches Rules, Categories, and Delete Rules from Airtable.
  Delete Rules (Optional)  
     If a delete rule matches, the email is removed.

  🔹 Result:  
  A dynamic, updatable rule system ensuring emails are handled properly.

  Final Outcome
  Daily Contact Sync** keeps contacts up to date.
  AI-driven email workflow** ensures smart categorisation.
  Outlook automatically updated** with AI-generated categories and importance.

  This automated system saves time, ensures efficient inbox management, and allows for customisable rules via Airtable.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.microsoftOutlook","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.if","n8n-nodes-base.microsoftOutlook","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.mondayCom","n8n-nodes-base.airtable","n8n-nodes-base.markdown","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.splitInBatches","n8n-nodes-base.merge","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","email management","AI/ML"],"complexity":"complex","use_cases":["Automated email categorisation and prioritisation","Daily contact synchronization from Monday.com to Airtable","Enriching emails with contact data for context-aware routing","Applying Airtable-driven rules including delete rules","Updating Outlook categories and importance flags automatically"],"estimated_setup_minutes":60,"required_services":["Microsoft 365 / Outlook (Office 365 credentials)","Monday.com (API token and board)","Airtable (API key / PAT and base)","OpenAI (API key)"],"key_features":["Daily contact sync (Monday.com → Airtable) with upserts","Sanitise and convert emails to AI‑friendly format","Contact matching/enrichment from Airtable","AI agent (OpenAI) for category, subcategory, priority and rationale","Structured output parsing to enforce JSON responses from AI model"],"target_audience":["IT administrators / integrators","Customer support and account management teams","Sales and operations teams managing contacts and inboxes by rulesets"]}


name: Scrape any web page into structured JSON data with ScrapeNinja and AI
author_name: Anthony
author_username: scrapeninja
categories: []
created_at: 2025-01-28T07:07:34.371Z
updated_at: 2025-01-28T07:07:34.371Z
url: https://n8n.io/workflows/2812
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  Disclaimer: This template only works on self-hosted for now, as it uses a community node.

  Use Case
  Web scrapers often break due to web page layout changes.
  This workflow attempts to mitigate this problem by auto-generating web scraping data extractor code via LLM.

  How It Works
  This workflow leverages ScrapeNinja n8n community node to: 
  scrape webpage HTML, 
  feed it into LLM (Google Gemini) and ask to write a JS extractor function code, then it 
  executes the written JS extractor against scraped HTML to extract useful data from webpage (the code is safely executed in a sandbox)

  Installation
  To install ScrapeNinja n8n node, in your self-hosted instance, go to Settings -&gt; Community nodes, enter "n8n-nodes-scrapeninja", and install. 

  Make sure you are using at least v0.3.0. 

  See this in action:
  https://www.linkedin.com/feed/update/urn:li:activity:7289659870935490560/

nodes_used:
  ["CUSTOM.scrapeNinja","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.manualTrigger","CUSTOM.scrapeNinja","@n8n/n8n-nodes-langchain.chainLlm","CUSTOM.scrapeNinja"]

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metadata_json:
  {"categories":["automation","web-scraping","data-processing","AI-assisted"],"complexity":"medium","use_cases":["Resilient web scraping that adapts to page layout changes","Automatically generate JS extractors for arbitrary pages","Convert scraped HTML into structured JSON","Periodic monitoring and data extraction from websites"],"estimated_setup_minutes":20,"required_services":["Self-hosted n8n instance","n8n community node: n8n-nodes-scrapeninja (v0.3.0+)","Google Gemini / Google Cloud AI credentials for lmChatGoogleGemini node","Internet access for scraping and LLM calls"],"key_features":["Uses ScrapeNinja node to fetch page HTML","Calls Google Gemini LLM to generate a JS extractor function","Executes the generated extractor in a sandbox to produce structured JSON","Reduces fragility of scrapers by auto-regenerating extraction logic","Works with community nodes (self-hosted only)"],"target_audience":["Developers","Data engineers","Analysts and researchers who need structured web data extraction」「n8n users running self-hosted instances"]}


name: Automate Content Generator for WordPress with DeepSeek R1
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-28T11:15:09.521Z
updated_at: 2025-01-28T11:15:09.521Z
url: https://n8n.io/workflows/2813
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow is designed to generate SEO-friendly content with DeepSeek R1 (or V3), publish it on WordPress, and update a Google Sheets document with the details of the created post. Below is a detailed analysis of what each node in the workflow does:

  How It Works
  Triggering the Workflow:
     The workflow starts with a Manual Trigger node, which is activated when the user clicks "Test workflow" in the n8n interface.

  Fetching Data:
     The Get Ideas node retrieves data from a Google Sheets document. It reads a specific sheet and filters the data based on the "ID POST" column, returning the first matching row.

  Setting the Prompt:
     The Set your prompt node extracts the PROMPT field from the Google Sheets data and assigns it to a variable for use in subsequent steps.

  Generating Content:
     The Generate article node uses an AI model (DeepSeek) to create an SEO-friendly article based on the prompt. The article includes an introduction, 2-3 chapters, and a conclusion, formatted in HTML.
     The Generate title node uses the same AI model to generate a concise, SEO-optimized title for the article.

  Publishing on WordPress:
     The Create post on WordPress node creates a new draft post on WordPress using the generated title and article content.

  Generating and Uploading an Image:
     The Generate Image node creates a photorealistic image based on the article title using an AI model (OpenAI).
     The Upload image node uploads the generated image to WordPress as a media file.
     The Set Image node assigns the uploaded image as the featured image for the WordPress post.

  Updating Google Sheets:
     The Update Sheet node updates the original Google Sheets document with the post details, including the title, post ID, creation date, and row number.

  Set Up Steps
  Configure Google Sheets Integration:
     Set up the Google Sheets node to connect to your Google account and specify the document ID and sheet name to read from and update.

  Set Up AI Models:
     Configure the OpenAI nodes (for generating the article, title, and image) with the appropriate API credentials and model settings (e.g., deepseek-reasoner for text generation).

  Configure WordPress Integration:
     Set up the WordPress node with your WordPress site's API credentials to allow creating posts and uploading media.

  Define the Prompt and Content Structure:
     In the Set your prompt node, ensure the prompt variable is correctly mapped to the data from Google Sheets.
     In the Generate article and Generate title nodes, define the instructions for the AI model to generate the desired content.

  Set Up Image Generation:
     Configure the Generate Image node with the appropriate prompt and image settings (e.g., size, quality, style).

  Configure HTTP Requests for Media Upload:
     Set up the Upload image and Set Image nodes to use the WordPress REST API for uploading and assigning the featured image.

  Map Data for Google Sheets Update:
     In the Update Sheet node, map the relevant fields (e.g., title, post ID, date) to the appropriate columns in the Google Sheets document.

  Test and Activate the Workflow:
     Run the workflow manually to ensure all steps execute correctly.
     Once verified, activate the workflow for automated execution.

  Overall purpose of the workflow
  This workflow automates the creation of SEO-friendly content for a WordPress blog. Starting from a prompt extracted from a Google Sheets document, it generates an article, a title, and an image, publishes the post on WordPress, and updates the Google Sheets document with the details of the created post. This process is useful for blog managers who want to automate content creation and publishing.

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

nodes_used:
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metadata_json:
  {"categories":["automation","content creation","integration","AI/ML"],"complexity":"medium","use_cases":["Automated blog post creation from prompts","Publishing drafts to WordPress with featured images","Maintaining an editorial log in Google Sheets","Generating SEO-optimized titles and HTML-formatted articles","Rapid content prototyping for marketing teams"],"estimated_setup_minutes":45,"required_services":["Google Sheets (Google API)","OpenAI (or DeepSeek model endpoint)","WordPress REST API (site credentials)","Image generation API (OpenAI images or equivalent)"],"key_features":["Reads prompts from Google Sheets and filters by ID","Generates SEO-friendly article content (HTML) via AI","Creates SEO-optimized titles via AI","Generates photorealistic featured images","Uploads media to WordPress and assigns featured image to post draft(supports REST API calls via HTTP nodes if needed )   )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )  )"],"target_audience":["Content managers","Bloggers and editors","SEO specialists and marketers  developers/automation engineers"]}


name: 2-way Sync Google Contacts and Notion
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-01-28T14:00:09.385Z
updated_at: 2025-01-28T14:00:09.385Z
url: https://n8n.io/workflows/2814
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Sync your Google Contacts with your Notion database.

  You can filter contacts by label or sync all contacts.

  All contact information is kept in sync between Google and Notion.

  What happens if I delete a contact?

  It gets deleted on the other end.

  Google Contact deleted → Notion page deleted
  Notion page deleted → Google contact deleted

  But you can easily change this behavior by disabling the respective deletion nodes.

  Who is this template for?

  Service providers looking to keep track of client information
  Organized individuals who like to centralize their lives in Notion
  Automation nerds who enjoy creating interesting projects

  How it works

  First, you perform an initial import to bring your contacts from Google to Notion.

  After that, the workflow listens for changes in both Notion and Google and keeps everything in sync.

  How to set up

  Instructions are included within the workflow itself.

  Check out my other templates

  👉 https://n8n.io/creators/solomon/

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  {"categories":["integration","automation","data synchronization","productivity"],"complexity":"complex","use_cases":["Keep Google Contacts and Notion contact database in sync","Centralize client or personal contact data in Notion","Automate two-way updates and deletions between systems","Initial bulk import of contacts into Notion followed by continuous sync"],"estimated_setup_minutes":30,"required_services":["Google Contacts (Google account / Contacts API)","Notion (Notion workspace / Notion API)","n8n instance (cloud or self-hosted with reachable webhook endpoint)"],"key_features":["Two-way real-time sync between Google Contacts and Notion","Initial import to seed Notion from Google Contacts","Configurable filtering by Google Contact label or sync all contacts","Bidirectional deletion propagation (can be disabled per node)","Change listeners on both Notion and Google to keep records aligned"],"target_audience":["Service providers managing client contact records","Individuals centralizing personal info in Notion","Automation enthusiasts building sync workflows"]}


name: Auto-Tag Blog Posts in WordPress with AI
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-01-28T16:50:58.256Z
updated_at: 2025-01-28T16:50:58.256Z
url: https://n8n.io/workflows/2816
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
description:
  How it works:
  This workflow automates tagging for WordPress posts using AI:

  Fetch blog post content and metadata.
  Generate contextually relevant tags using AI.
  Verify existing tags in WordPress and create new ones if necessary.
  Automatically update posts with accurate and optimized tags.

  Set up steps:

  Estimated time: ~15 minutes.

  Configure the workflow with your WordPress API credentials.
  Connect your content source (e.g., RSS feed or manual input).
  Adjust tag formatting preferences in the workflow settings.
  Run the workflow to ensure proper tag creation and assignment.

  This workflow is perfect for marketers and content managers looking to streamline their content categorization and improve SEO efficiency.

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metadata_json:
  {"categories":["automation","content management","SEO","AI integration","integration"],"complexity":"medium","use_cases":["Automatically generate and assign tags to WordPress posts","Improve SEO and content discoverability via consistent tagging","Bulk tag creation and taxonomy management","Enrich post metadata from RSS or other content sources","Automate repetitive content categorization tasks"],"estimated_setup_minutes":15,"required_services":["WordPress REST API (site credentials)","AI provider (OpenAI or compatible LM via lmChatOpenAi)","Content source (RSS feed or other HTTP input/webhook)","n8n instance"],"key_features":["Fetch post content and metadata from WordPress or RSS","Generate contextually relevant tags using an AI model","Verify existing tags and create new WordPress tags if needed","Automatically update posts with optimized tags","Configurable tag formatting and workflow settings (e.g., casing, delimiters)"],"target_audience":["content managers","marketers","SEO specialists"]}


name: AI Social Media Caption Creator creates social media post captions in Airtable
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2025-01-29T08:07:40.999Z
updated_at: 2025-01-29T08:07:40.999Z
url: https://n8n.io/workflows/2817
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Welcome to my AI Social Media Caption Creator Workflow!

  What this workflow does
  This workflow automatically creates a social media post caption in an editorial plan in Airtable. It also uses background information on the target group, tonality, etc. stored in Airtable.

  This workflow has the following sequence:

  Airtable trigger (scan for new records every minute)
  Wait 1 Minute so the Airtable record creator has time to write the Briefing field
  retrieval of Airtable record data
  AI Agent to write a caption for a social media post. The agent is instructed to use background information stored in Airtable (such as target group, tonality, etc.) to create the post.
  Format the output and assign it to the correct field in Airtable.
  Post the caption into Airtable record.

  Requirements
  Airtable Database: Documentation
  AI API access (e.g. via OpenAI, Anthropic, Google or Ollama)

  Example of an editorial plan in Airtable:
  Editorial Plan example in Airtable

  For this workflow you need the Airtable fields "created_at", "Briefing" and "SoMe_Text_AI"

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

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name: Fetch Scriptures Dynamically from get Bible API
author_name: getBible
author_username: christian
categories: []
created_at: 2025-01-29T13:42:38.278Z
updated_at: 2025-01-29T13:42:38.278Z
url: https://n8n.io/workflows/2818
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Overview
  The Get Bible Query Workflow is a modular and self-standing workflow designed to retrieve scriptures dynamically based on structured input. It serves as an intermediary layer that extracts references, queries the GetBible API, and returns scriptures in a standardized JSON format.

  This workflow is fully prepared for integration—simply call it from another workflow with the required JSON input, and it will return the requested scripture data.

  Who Is This For?
  This workflow is ideal for developers, Bible study apps, research tools, and dynamic scripture-based projects that need seamless access to scriptural content without direct API interaction.

  ✅ Use Cases:
  Bible Study Apps** → Embed scripture retrieval functionality.
  Research & Theology Tools** → Fetch structured verse data.
  Dynamic Content Generation** → Integrate real-time scripture references.
  Sermon Preparation** → Automate scripture lookups.

  How It Works
  Trigger Workflow → This workflow is designed to be called from another workflow with a structured JSON input.
  Receive Input → Accepts a JSON object containing references, translation, and API version.
  Extract References → Parses single verses, comma-separated lists, and ranged passages.
  Query API → Sends structured requests to the GetBible API.
  Format Response → Returns structured JSON output, maintaining API response consistency.

  JSON Input Structure
  References** → Should include the book name, chapter, and verse(s).
  Multiple Verses** → Separated by commas (e.g., John 3:16,18).
  Verse Ranges** → Defined with a dash (e.g., John 3:16-18).
  Translation** → Choose from the supported translations.
  API Version** → Currently supports v2.

  Example JSON Input
  {
    "references": [
      "1 John 3:16",
      "Jn 3:16",
      "James 3:16",
      "Rom 3:16"
    ],
    "translation": "kjv",
    "version": "v2"
  }

  Example API Response
  {
    "result": {
      "kjv_62_3": {
        "translation": "King James Version",
        "abbreviation": "kjv",
        "book_name": "1 John",
        "chapter": 3,
        "ref": ["1 John 3:16"],
        "verses": [
          {
            "chapter": 3,
            "verse": 16,
            "name": "1 John 3:16",
            "text": "Hereby perceive we the love of God, because he laid down his life for us: and we ought to lay down our lives for the brethren."
          }
        ]
      }
    }
  }

  💡 Fully structured and formatted response – ready for seamless integration.

  Integration and Usage
  The GetBible Query Workflow is designed for immediate use. Simply call it from another workflow and pass the appropriate JSON object as input, and it will return the requested scripture passages.

  ✔️ No additional configuration is required.
  ✔️ Designed for fast, reliable, and structured scripture retrieval.
  ✔️ Fully compatible with GetBible API responses.

  Why Use This Workflow?
  ✔️ Fast & Reliable → Direct API integration for efficient queries.
  ✔️ Flexible Queries → Supports single, multi-verse, and ranged requests.
  ✔️ Agent-Compatible → Easily integrates into automated workflows.
  ✔️ No Code Needed → Just configure the JSON input and run the workflow.

  Next Steps
  🔗 API Support
  📖 API Documentation
  💬 Need help? Join the community for support! 🚀

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metadata_json:
  {"categories":["integration","data retrieval","automation"],"complexity":"medium","use_cases":["Embed scripture retrieval in apps or websites","Generate dynamic content with real-time scripture references","Research and theology tooling to fetch structured verse data","Automate sermon preparation and scripture lookups","Agent/workflow-driven scripture queries"],"estimated_setup_minutes":10,"required_services":["GetBible API","n8n (caller workflow runtime)","Internet access"],"key_features":["Parses single verses, comma‑separated lists, and verse ranges","Queries GetBible API and normalizes responses to consistent JSON","Callable as a standalone workflow from other n8n workflows","Supports translation selection and API versioning","Returns fully structured, ready‑to‑use scripture objects"],"target_audience":["Developers building Bible study apps or integrations","No‑code/low‑code automators using n8n","Researchers and theologians needing structured verse data」「Pastors and content creators preparing sermons or devotional content"]}


name: Simple Expense Tracker with n8n Chat, AI Agent and Google Sheets
author_name: Anthony
author_username: scrapeninja
categories: []
created_at: 2025-01-29T13:43:53.212Z
updated_at: 2025-01-29T13:43:53.212Z
url: https://n8n.io/workflows/2819
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:31
description:
  Use Case
  It is very convenient to add expenses via simple chat message. This workflow attempts to do exactly this using AI-powered n8n magic!


  Send message to a chat, something like "car wash; 59.3 usd; 25 jan 2024"

  And get a response:
  Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"}


  LLM will smartly parse your message to structured JSON and save the expense as a new row into Google Sheet!

  Installation
  1. Set up Google Sheets:
  Clone this Sheet:
  https://docs.google.com/spreadsheets/d/1D0r3tun7LF7Ypb21CmbTKEtn76WE-kaHvBCM5NdgiPU/edit?gid=0#gid=0

  (File -&gt; Make a copy)

  Choose this sheet into "Save expense into Google Sheets" node.

  2. Fix sub-workflow dropdown: 
  open "Parse msg and save to Sheets" node (which is an n8n sub-workflow executor tool) and make sure the SAME workflow is chosen in the dropdown. it will allow n8n to locate and call "Workflow Input Trigger" properly when needed.

  3. Activate the workflow to make chat work properly.
  Sent message to chat, something like "car wash; 59.3 usd; 25 jan 2024"

  you should get a response:
  Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"}

  and new row in Google sheets should be inserted!

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

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","chatbot","finance"],"complexity":"medium","use_cases":["Quick conversational expense logging","Personal finance / expense tracking","Team expense submissions into a shared sheet","Automated structured extraction of expense details from free-text messages"],"estimated_setup_minutes":15,"required_services":["n8n (self‑hosted or cloud)","Google Sheets (Google account)","OpenAI Chat API (or compatible LLM via lmChatOpenAi node)"],"key_features":["Chat-based input for adding expenses","LLM-powered parsing of free-text into structured JSON","Automatic insertion of parsed data as rows in Google Sheets","Uses a sub-workflow to parse and save entries (modular)","Includes conversational memory and agent nodes for robust handling"],"target_audience":["n8n users / automation builders","Freelancers and individuals tracking expenses","Small teams wanting simple shared expense logging"]}


name: Backup Workflows to Git Repository on Gitea
author_name: Octoleo
author_username: octoleo
categories: []
created_at: 2025-01-29T14:22:41.191Z
updated_at: 2025-01-29T14:22:41.191Z
url: https://n8n.io/workflows/2820
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Overview
  This workflow automates the backup of all workflows from your system to a Git repository hosted on Gitea. It runs on a scheduled trigger, fetching, encoding, and committing workflow data, ensuring seamless version control and disaster recovery.

  📌 Quick Setup: Just update three global variables and configure authentication—no manual exports needed!

  How It Works (Quick Glance)
  1️⃣ Scheduled Execution → Runs automatically at defined intervals.
  2️⃣ Fetch Workflows → Uses the API to retrieve all workflows.
  3️⃣ Process Workflows → Converts workflow data into a Git-friendly format.
  4️⃣ Commit & Push to Git → Saves workflows in a Gitea repository.

  Setup Steps (⚡ Takes ~5 min)

  1️⃣ Set Global Variables
  Go to the Globals section in the workflow and update:
  repo.url* → https://your-gitea-instance.com *(Replace with your actual Gitea URL)
  repo.name* → workflows *(Repository name where backups will be stored)
  repo.owner* → octoleo *(Gitea account that owns the repository)

  📌 These three variables define where the workflows are stored.

  2️⃣ Configure Gitea Authentication
  Go to your Gitea account* → Generate a *Personal Access Token**
  In the credential manager, create a new Gitea Token with:
  Name:** Authorization
  Value:** Bearer YOUR_PERSONAL_ACCESS_TOKEN

  📌 Ensure there is a space after Bearer before the token!

  3️⃣ Link Credentials to Git Nodes
  Attach the Gitea credentials to these three Git nodes:
  GetGitea** → Retrieves existing repository data
  PutGitea** → Updates workflows
  PostGitea** → Adds new workflows

  4️⃣ Link Credentials for API Requests
  Add API authentication** in the node that fetches all workflows.

  5️⃣ Test & Activate
  Run the workflow manually** to confirm backups work.
  Enable the schedule trigger for automation.

  📌 The workflow automatically checks for changes before committing updates.

  Why Use This Workflow?
  ✅ Automated Backups → No manual exports needed.
  ✅ Version Control → Easily track workflow changes.
  ✅ Simple Setup → Just configure globals & credentials.
  ✅ Secure → Uses token-based authentication.

  Next Steps
  💬 Have questions? Reach out on the forum! 🚀

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

workflow_json_compressed:
  [encoded data, 5380 characters]

metadata_json:
  {"categories":["automation","backup","integration","devops","version-control"],"complexity":"simple","use_cases":["Automated backups of n8n workflows","Version control for workflow changes","Disaster recovery and restore","Audit trail of workflow edits","Periodic export for migration or review"],"estimated_setup_minutes":5,"required_services":["n8n instance (API access to workflows)","Gitea (self-hosted or hosted) with repository","Gitea Personal Access Token (Bearer token)"],"key_features":["Scheduled automatic exports of all workflows","Fetches workflows via API and processes into Git-friendly files","Commits and pushes updates to a Gitea repository","Checks for changes before committing to avoid noise","Uses global variables for repo configuration (url, owner, name)"],"target_audience":["n8n administrators","DevOps engineers","IT/system administrators"]}


name: Domain Outbound : Automate Lead Extraction, and Targeted Outreach
author_name: Badr
author_username: b4dr
categories: []
created_at: 2025-01-30T13:57:48.318Z
updated_at: 2025-01-30T13:57:48.318Z
url: https://n8n.io/workflows/2821
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Domain Outbound Machine: Automate Lead Generation, Email Extraction, and Targeted Outreach with n8n

  Description  
  Domain Outbound Machine is an n8n workflow designed to fully automate the domain sales process: lead generation, email extraction, personalized outreach, and automated email sending. It also stores extracted email addresses and sent emails in an Excel file for tracking and follow-up.

  How It Works?

  1. Domain Input  
  You provide the domain name you want to sell. The workflow takes this domain as the starting point.

  2. Google Maps Query Generation  
  The workflow automatically generates 100 Google Maps queries based on the domain's niche or industry. This ensures you target businesses or individuals who might be interested in acquiring the domain.

  3. Web Scraping for Emails  
  Using the generated queries, the workflow scrapes relevant websites to extract email addresses. This step ensures you have a targeted list of potential buyers.

  4. Email Enrichment  
  For each extracted email, the workflow gathers publicly available information (e.g., LinkedIn, company websites) to enhance personalization.

  5. Personalized Email Creation  
  Based on the enriched data, the workflow crafts a custom email for each lead, emphasizing the domain’s value, its relevance to their business, and a compelling call-to-action.

  6. Email Sending via Gmail  
  The workflow sends the personalized emails directly through your Gmail account, ensuring timely and professional outreach.

  7. Email & Lead Tracking in Excel  
  All extracted email addresses and sent messages are automatically stored in an Excel file, allowing you to track responses and manage follow-ups efficiently.

  Key Features
  ✅ Automated Lead Generation – Saves time by automatically finding and extracting potential leads.  
  ✅ Personalized Outreach – Boosts engagement by tailoring each email to the recipient.  
  ✅ Seamless Integration – Uses n8n’s automation to connect Google Maps, web scraping tools, and Gmail in one workflow.  
  ✅ Scalable & Trackable – Adjust the number of queries or emails as needed, while keeping track of everything in Excel.  

  Domain Outbound Machine is perfect for domain investors, digital marketers, and anyone looking to sell domains efficiently. By automating the tedious tasks, you can focus on closing deals and growing your business.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.removeDuplicates","n8n-nodes-base.removeDuplicates","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.wait","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.filter","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","lead generation","sales outreach","data enrichment","integration"],"complexity":"complex","use_cases":["Automated domain sales outreach","Bulk lead generation and email extraction","Personalized cold email campaigns","Lead enrichment and tracking","Building targeted prospect lists for sales"],"estimated_setup_minutes":90,"required_services":["Gmail account (SMTP/Google API)","Google Sheets / Excel (storage)","Google Maps or Google Search (for query/results)","OpenAI (or other AI nodes used for personalization)","Web scraping/HTTP access (may require proxies or CAPTCHA handling)","Public data sources for enrichment (LinkedIn/company websites)"],"key_features":["Generate targeted Google Maps/search queries from a domain niche","Web scrape sites to extract email addresses","Enrich emails with publicly available data for personalization","Create AI-assisted personalized email content","Send emails via Gmail account directly from the workflow」「Store and track extracted leads and sent messages in Excel/Google Sheets for follow-up"],"target_audience":["Domain investors and brokers","Digital marketers and growth teams","Sales development representatives (SDRs) and founders doing outreach」「Lead generation specialists and agencies"]}


name: AI-Generated Summary Block for WordPress Posts
author_name: Dataki
author_username: dataki
categories: []
created_at: 2025-01-30T15:04:16.233Z
updated_at: 2025-01-30T15:04:16.233Z
url: https://n8n.io/workflows/2822
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  What is this workflow?  
  This n8n template automates the process of adding an AI-generated summary at the top of your WordPress posts.  
  It retrieves, processes, and updates your posts dynamically, ensuring efficiency and flexibility without relying on a heavy WordPress plugin.  

  Example of AI Summary Section

  How It Works  
  Triggers → Runs on a scheduled interval or via a webhook when a new post is published.  
  Retrieves posts → Fetches content from WordPress and converts HTML to Markdown for AI processing.  
  AI Summary Generation → Uses OpenAI to create a concise summary.  
  Post Update → Inserts the summary at the top of the post while keeping the original excerpt intact.  
  Data Logging & Notifications → Saves processed posts to Google Sheets and notifies a Slack channel.  

  Why use this workflow?  
  ✅ No need for a WordPress plugin → Keeps your site lightweight.  
  ✅ Highly flexible → Easily connect with Google Sheets, Slack, or other services.  
  ✅ Customizable → Adapt AI prompts, formatting, and integrations to your needs.  
  ✅ Smart filtering → Ensures posts are not reprocessed unnecessarily.  

  💡 Check the detailed sticky notes for setup instructions and customization options!  

nodes_used:
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metadata_json:
  {"categories":["automation","content-generation","CMS","integration"],"complexity":"complex","use_cases":["Automatically add AI-generated summaries to WordPress posts","Automate content enrichment and editorial workflows","Log processed posts and notify teams via Slack","Replace plugin-based solutions to keep sites lightweight"],"estimated_setup_minutes":30,"required_services":["WordPress (REST API access)","OpenAI (or compatible LLM via lmChatOpenAi)","Google Sheets","Slack","n8n (hosted or self-hosted webhook access)"],"key_features":["Scheduled or webhook-triggered execution","Fetches WordPress posts and converts HTML to Markdown for AI processing","Generates concise summaries with OpenAI and inserts them at top of posts","Preserves original excerpt and avoids reprocessing using smart filters","Logs processed posts to Google Sheets and sends Slack notifications for updates or errors"],"target_audience":["developers","content managers","site owners / administrators of WordPress sites with many posts"]}


name: Social Media Analysis and Automated Email Generation
author_name: PollupAI
author_username: zeerobug
categories: []
created_at: 2025-01-30T16:19:16.536Z
updated_at: 2025-01-30T16:19:16.536Z
url: https://n8n.io/workflows/2823
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  Social Media Analysis and Automated Email Generation

  &gt; by Thomas Vie Thomas@pollup.net

  Who is this for?
  This template is ideal for marketers, lead generation specialists, and business professionals seeking to analyze social media profiles of potential leads and automate personalized email outreach efficiently.

  What problem is this workflow solving?
  Manually analyzing social media profiles and crafting personalized emails can be time-consuming and prone to errors. This workflow streamlines the process by integrating social media APIs with AI to generate tailored communication, saving time and increasing outreach effectiveness.

  What this workflow does:
  Google Sheets Integration: Start with a Google Sheet containing lead information such as LinkedIn URL, Twitter handle, name, and email.
  Social Media Data Extraction: Automatically fetch profile and activity data from Twitter and LinkedIn using RapidAPI integrations.
  AI-Powered Content Generation: Use OpenAI's Chat Model to analyze the extracted data and generate personalized email subject lines and cover letters.
  Automated Email Dispatch: Send the generated email directly to the lead, with a copy sent to yourself for tracking purposes.
  Progress Tracking: Update the Google Sheet to indicate completed actions.

  Setup:
  Google Sheets:
     Create a sheet with the columns: LinkedIn URL, name, Twitter handle, email, and a "done" column for tracking.
     Populate the sheet with your leads.

  RapidAPI Accounts:
     Sign up for RapidAPI and subscribe to the Twitter and LinkedIn API plans.
     Configure API authentication keys in the workflow.

  AI Configuration:
     Connect OpenAI Chat Model with your API key for text generation.

  Email Integration:
     Add your email credentials or service (SMTP or third-party service like Gmail) for sending automated emails.

  How to customize this workflow to your needs:
  Modify the AI Prompt:** Adapt the prompt in the AI node to better align with your tone, style, or specific messaging framework.
  Expand Data Fields:** Add additional data fields in Google Sheets if you require further personalization.
  API Limits:** Adjust API configurations to fit your usage limits or upgrade to higher tiers for increased data scraping capabilities.
  Personalize Email Templates:** Tweak email formats to suit different audiences or use cases.
  Extend Functionality:** Integrate additional social media platforms or CRM tools as needed.

  By implementing this workflow, you’ll save time on repetitive tasks and create more effective lead generation strategies.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.emailSend","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","marketing","integration","data processing","AI-driven outreach"],"complexity":"medium","use_cases":["lead generation","personalized outreach","social media profiling","sales prospecting","CRM enrichment"],"estimated_setup_minutes":45,"required_services":["Google Sheets","RapidAPI (Twitter API, LinkedIn API)","OpenAI (Chat model)","Email service (SMTP or Gmail)"],"key_features":["Automatic extraction of social profile and activity data","AI-powered generation of personalized email subject lines and cover letters","Automated email dispatch with copy to sender","Progress tracking and status updates in Google Sheets","Configurable AI prompts and email templates"],"target_audience":["marketers","lead generation specialists","sales professionals and SDRs\",\"growth hackers\",\"recruiters\",\"small business owners"]}


name: Query Perplexity AI from your n8n workflows
author_name: Emmanuel Bernard
author_username: n8ninja
categories: []
created_at: 2025-01-30T18:22:09.713Z
updated_at: 2025-01-30T18:22:09.713Z
url: https://n8n.io/workflows/2824
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow illustrates how to use Perplexity AI in your n8n workflow. 

  Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.

  Credentials Setup 

  1/ Go to the perplexity dashboard, purchase some credits and create an API Key

  https://www.perplexity.ai/settings/api

  2/ In the perplexity Request node, use Generic Credentials, Header Auth. 

  For the name, use the value "Authorization"
  And for the value "Bearer pplx-e4...59ea" (Your Perplexity Api Key)

  AI Model 
  Sonar Pro is the current top model used by perplexity. 
  If you want to use a different one, check this page: 

  https://docs.perplexity.ai/guides/model-cards

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

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metadata_json:
  {"categories":["automation","integration","ai","knowledge management"],"complexity":"simple","use_cases":["Ask Perplexity AI questions from n8n workflows","Enrich automation with real-time AI answers","Automated research and summarization","Improve chatbot or support responses","Generate content ideas or quick briefs"],"estimated_setup_minutes":10,"required_services":["Perplexity AI API (API key)","n8n instance (with HTTP/Webhook capability)"],"key_features":["Send queries to Perplexity via HTTP node","Header (Bearer) authentication credential setup","Selectable AI model (e.g., Sonar Pro)","Manual or webhook trigger support","Returns real-time, sourced answers for workflow use"],"target_audience":["Developers","Automation engineers","Product managers/Owners integrating AI features into apps/workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed workflowsed"]}


name: Spot Workplace Discrimination Patterns with AI
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-01-30T19:31:39.617Z
updated_at: 2025-01-30T19:31:39.617Z
url: https://n8n.io/workflows/2825
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
description:
  How It Works:
  • Scrapes company review data from Glassdoor using ScrapingBee.
  • Extracts demographic-based ratings using AI-powered text analysis.
  • Calculates workplace disparities with statistical measures like z-scores, effect sizes, and p-values.
  • Generates visualizations (scatter plots, bar charts) to highlight patterns of discrimination or bias.

  Example Visualizations:

  Set Up Steps:
  Estimated time: ~20 minutes.
  • Replace ScrapingBee and OpenAI credentials with your own.
  • Input the company name you want to analyze (best results with large U.S.-based organizations).
  • Run the workflow and review the AI-generated insights and visual reports.

  This workflow empowers users to identify potential workplace discrimination trends, helping advocate for greater equity and accountability.

  Additional Credit: Wes Medford
  For algorithms and inspiration

nodes_used:
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metadata_json:
  {"categories":["data processing","automation","AI/NLP","analytics"],"complexity":"complex","use_cases":["Detect workplace discrimination patterns from employee reviews","Extract demographic-based sentiment/ratings using AI","Compute statistical disparity measures (z-scores, effect sizes, p-values)","Generate visual reports (scatter plots, bar charts) for DEI analyses","Support HR audits, research, and advocacy efforts"],"estimated_setup_minutes":20,"required_services":["ScrapingBee (for Glassdoor scraping)","OpenAI (for AI text analysis)","QuickChart or charting service (for visualizations)","Glassdoor (source site)","n8n instance (to run workflow)"],"key_features":["Scrapes Glassdoor review data","AI-powered extraction of demographic-based ratings and sentiment","Calculates statistical measures of disparity (z-scores, effect sizes, p-values)","Generates visualizations: scatter plots and bar charts","Produces AI-generated insights and narrative explanations for findings"],"target_audience":["HR analysts and DEI teams","Data scientists and researchers","Compliance officers and auditors of workplace practices」「Advocates and nonprofit organizations focused on equity"]}


name: Automatically Send a Direct Message (DM) to New Followers on Bluesky using Baserow
author_name: Gareth B. Davies
author_username: garethbdavies
categories: []
created_at: 2025-01-30T21:22:55.627Z
updated_at: 2025-01-30T21:22:55.627Z
url: https://n8n.io/workflows/2827
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Send personalized welcome messages to your new Bluesky followers automatically, helping you maintain engagement while saving time.

  This workflow monitors your follower list and sends customized direct messages, creating a warm welcome for new connections without manual intervention.

  How it works
  Checks your Bluesky followers list daily at 9 AM
  Identifies new followers by comparing against a database
  Extracts the follower's first name** when available
  Sends a personalized welcome message with optional link
  Prevents duplicate messages** through double-verification
  Maintains a record of sent messages to avoid repetition

  Set up steps (10-15 minutes)
  Create a Bluesky account if you haven't already
  Generate an app password in your Bluesky settings
  Enter your Bluesky handle and app password in the "Set Bluesky Credentials" node
  Set up your database (Baserow, or adapt for Airtable/Google Sheets)
  Customize your welcome message in the "Create Welcome Message" node
  Optional: Adjust the regular check time (default: 9 AM)

  Features
  Personalized messaging** using follower's first name when available
  Database tracking** to prevent duplicate messages
  Basic rate limiting protection** to stay within API limits
  Customizable welcome message with clickable links
  Ability to handle up to 100 new followers per check

  Perfect for creators who want to
  Welcome new followers consistently
  Save time** on manual messaging
  Build early engagement with followers
  Share important links or resources
  Maintain a professional presence
  Scale** their community management

  Suggested enhancements
  Add message templates for different follower types
  Include email/Slack notifications for errors
  Add analytics tracking for message success rates
  Implement dynamic timing based on follower activity
  Create A/B testing for different welcome messages
  Add follower segmentation based on profile data

nodes_used:
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metadata_json:
  {"categories":["automation","social media","community management"],"complexity":"medium","use_cases":["Automatically welcome new followers","Increase early engagement with new connections","Scale community onboarding","Maintain records of outreach to avoid duplicates","Share links/resources with new followers"],"estimated_setup_minutes":15,"required_services":["Bluesky (API/app password)","Baserow (or Airtable/Google Sheets as alternative)"],"key_features":["Daily scheduled check for new followers","Personalized DM using follower first name when available","Database tracking to prevent duplicate messages","Double-verification to avoid repeats","Basic rate-limiting protection and batch handling (up to ~100 new followers per run)"],"target_audience":["creators","community managers","social media managers', 'influencers', 'solopreneurs"]}


name: Batch Airtable requests to send data 9x faster
author_name: Brahim HAMICHAN
author_username: brahimh
categories: []
created_at: 2025-02-01T06:21:25.311Z
updated_at: 2025-02-01T06:21:25.311Z
url: https://n8n.io/workflows/2831
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Watch Demo YouTube Video

  Optimized Airtable Bulk Data Workflow

  This workflow is specifically designed to address the challenges of upserting or inserting large volumes of data into Airtable. By leveraging the Airtable Batch API, it delivers up to 9X faster performance compared to standard data insertion methods, making it an indispensable tool for high-demand data operations.

  Key Features
  •	Accelerated Data Processing:
  Utilize the Airtable Batch API to perform bulk operations swiftly and efficiently.
  •	Seamless Workflow Integration:
  Easily integrate this sub-processor into any n8n workflow that requires Airtable updates, ensuring smooth data synchronization across multiple processes.
  •	Enhanced Reliability and Scalability:
  Designed to handle extensive datasets, this solution is perfect for real-time updates, database migrations, and continuous data syncing without performance degradation.

  Setup Instructions
  Add the Sub-Workflow:
  Import this workflow to your n8n workflows, then add it as a sub-workflow call in other workflows requiring a lot of Airtable updates.
  **Configure Sub-Worflow variables:
  "set_Batching_vars" SET Node**
  •	Obtain the correct Base ID and Table ID, and insert in the "set_Batching_vars" SET Node.

  •	Add or select the correct Airtable credentials in both Airtable Upsert & Insert HTTP nodes in the sub-workflow.

  •	Ensure the API permissions are set correctly to allow data insertion/upsertion.
  **Adjust Batch Settings:
  "set_Batching_vars" SET Node**
  •	In the same "set_Batching_vars" SET Node, put the field name in the "merge_on" field if you wish to upsert record, otherwise, keep it empty for insertion.

  •	Correctly setup the fields you want to insert/upsert in the 'record' field.

  Test the Integration:
  Run a small-scale test to ensure that data is correctly processed and inserted/upserted into Airtable.

  Use Case Scenarios

  •	Bulk Data Insertion:
  Efficiently insert large datasets into Airtable, perfect for initial data migrations or periodic data updates.
  •	Real-Time Data Upsertion:
  Keep your Airtable records current by integrating this workflow with your live data pipelines.
  •	Database Migrations & Synchronization:
  Seamlessly transfer data between databases and Airtable, ensuring minimal downtime and data integrity.

  Specific Requirements for Airtable Integration
  •	Airtable Account:
  You must have an active Airtable account with appropriate permissions to modify the target base.
  •	API Credentials:
  Secure a valid Airtable API connection and ensure you have the correct Base ID and Table ID for the target data store.

  By integrating this workflow into your system, you can significantly improve the efficiency of your Airtable operations, reducing processing time and enabling smoother data management at scale.

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metadata_json:
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name: Scrape Latest 20 TechCrunch Articles
author_name: Teddy
author_username: teddysbeach
categories: []
created_at: 2025-02-01T07:06:30.056Z
updated_at: 2025-02-01T07:06:30.056Z
url: https://n8n.io/workflows/2832
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
description:
  Retrieve 20 Latest TechCrunch Articles

  Who is this for?
  This workflow is designed for developers, content creators, and data analysts who need to scrape recent articles from TechCrunch. It’s perfect for anyone looking to aggregate news articles or create custom feeds for analysis, reporting, or integration into other systems.

  What problem is this workflow solving?
  This workflow automates the process of scraping recent articles from TechCrunch. Manually collecting article data can be time-consuming and inefficient, but with this workflow, you can quickly gather up-to-date news articles with relevant metadata, saving time and effort.

  What this workflow does
  This workflow retrieves the latest 20 news articles from TechCrunch’s “Recent” page. It extracts the article URLs, metadata (such as titles and publication dates), and main content for each article, allowing you to access the information you need without any manual effort.

  Setup
  Clone or download the workflow template.
  Ensure you have a working n8n environment.
  Configure the HTTP Request nodes with your desired parameters to connect to the TechCrunch API.
  (Optional) Customize the workflow to target specific sections or topics of interest.
  Run the workflow to retrieve the latest 20 articles.

  How to customize this workflow to your needs
  Modify the HTTP request to pull articles from different pages or sections of TechCrunch.
  Adjust the number of articles to retrieve by changing the selection criteria.
  Add additional processing steps to further filter or analyze the article data.

  Workflow Steps
  Send an HTTP request to the TechCrunch "Recent" page.
  Parse a posts box that holds the list of articles.
  Parse all posts to extract all articles.
  spilt out posts for each article.
  Extract the URL and metadata from each article.
  Send an HTTP request for each article using its URL.
  Locate and parse the main content of each article.

  Note: Be sure to update the HTTP Request nodes with any necessary headers or authentication to work with TechCrunch’s website.

nodes_used:
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metadata_json:
  {"categories":["automation","web-scraping","data-extraction","news-aggregation"],"complexity":"medium","use_cases":["Aggregate latest TechCrunch articles","Create custom news feeds","Content analysis and reporting","Content curation and monitoring"],"estimated_setup_minutes":15,"required_services":["n8n instance with internet access","TechCrunch public website (HTTP requests)","Optional: scraping proxy or CAPTCHA/bot mitigation service if needed"],"key_features":["Fetch latest 20 articles from TechCrunch Recent page","Extract article URLs, titles, publication dates and main content","HTML parsing and per-article requests","Configurable number of articles and target sections/pages","Split-and-process workflow for each article"],"target_audience":["Developers","Content creators","Data analysts"]}


name: Smart Email Outreach Sequence – AI-Powered & Customizable
author_name: Badr
author_username: b4dr
categories: []
created_at: 2025-02-01T10:42:30.224Z
updated_at: 2025-02-01T10:42:30.224Z
url: https://n8n.io/workflows/2833
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automated Email Outreach Workflow

  This intelligent email automation workflow helps you maximize engagement through domain-based outreach. It utilizes AI-powered personalization and strategic follow-ups to increase response rates. The workflow includes a customizable three-step email sequence (initial email + two follow-ups) sent at optimized 3-day intervals.

  🔥 Key Features
  ✅ Smart Domain Extraction – Automatically extracts domains from email addresses for precise targeting.  
  ✅ Website Intelligence – Uses Jina.ai to analyze recipient websites and craft ultra-relevant messaging.  
  ✅ AI-Powered Personalization – Leverages GPT-3.5 to generate compelling and highly customized email content.  
  ✅ Adjustable AI Prompts – Easily customize AI-generated messages to align with your product, service, or campaign.  
  ✅ Automated Follow-Up Sequence – Sends a structured series of three emails over time to increase engagement.  
  ✅ Human-Like Sending Patterns – Randomized delays help bypass spam filters and improve deliverability.  
  ✅ Error-Resilient Execution – Built-in error handling ensures a smooth, uninterrupted workflow.  

  🎯 Ideal Use Cases
  ✔️ Domain Sales & Lead Generation – Engage potential buyers with hyper-personalized emails.  
  ✔️ B2B Partnerships & Outreach – Automate connections with potential business partners.  
  ✔️ Cold Emailing at Scale – Improve engagement with structured, AI-optimized emails.  

  ⚙️ How It Works

  1️⃣ Setup & Configuration
  Integrate Gmail via OAuth for secure email sending.  
  Connect your Google Sheets with leads for automated processing.  
  Customize AI-generated messages by adjusting prompt inputs to fit your strategy.  

  2️⃣ Workflow Execution
  Domain Extraction – Automatically extracts recipient domains from email addresses.  
  Website Analysis – Uses Jina.ai to gather insights for ultra-personalized messaging.  
  AI-Powered Email Generation – GPT-3.5 crafts highly engaging outreach emails.  
  Automated Email Sending – Initial email is sent, followed by two optimized follow-ups if no response is received.  
  Engagement Tracking & Adaptation – Ensures delivery success and prevents emails from being flagged as spam.  

  ⚡ Setup Instructions

  ✅ Preconditions
  You need a Gmail account with OAuth authentication enabled.  
  Your Google Sheets must contain lead emails formatted correctly.  
  Ensure your n8n instance is running and connected to your workflow.  

  🛠 Implementation Steps
  Install and open n8n.  
  Import the workflow JSON file into your n8n instance.  
  Configure your Gmail credentials in the workflow settings.  
  Link your Google Sheets containing the leads' email addresses.  
  Customize AI prompts in the GPT-3.5 node to match your campaign strategy.  
  Test the workflow manually to ensure proper execution.  
  Activate the workflow and let automation take over!  

  📈 Expected Results
  🚀 Higher Response Rates – Personalization improves recipient engagement.  
  📊 Increased Conversions – Optimized email sequences boost lead-to-client conversions.  
  💡 Time-Saving Automation – Hands-free execution streamlines your outreach process.  

  🔧 Ready to supercharge your outreach? Set up the workflow and start automating today! 🚀🔥  

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metadata_json:
  {"categories":["automation","email outreach","sales & marketing","AI-enabled workflows"],"complexity":"complex","use_cases":["Domain-based sales & lead generation","B2B partnership outreach","Cold emailing at scale","Automated follow-up sequences","Personalized outreach using website insights"],"estimated_setup_minutes":30,"required_services":["n8n instance","Gmail (OAuth)","Google Sheets","OpenAI (GPT-3.5)","Jina.ai (website intelligence)"],"key_features":["Smart domain extraction from email addresses","Website analysis via Jina.ai for contextual personalization","AI-powered email generation (GPT-3.5) with adjustable prompts","Three-step automated follow-up sequence (optimized 3-day intervals)","Human-like randomized sending delays to improve deliverability and avoid spam filters','Built-in error handling and execution resilience','Integration with Google Sheets for lead management','Engagement tracking and conditional follow-ups based on responses"],"target_audience":["Sales teams / SDRs","Growth marketers","B2B founders and business development reps (BDRs) / partnership teams | outreach specialists (agencies) | n8n users looking to automate email sequences"]}


name: Automated Upwork Job Alerts with MongoDB & Slack
author_name: Artur
author_username: arlusm1
categories: []
created_at: 2025-02-01T15:08:11.227Z
updated_at: 2025-02-01T15:08:11.227Z
url: https://n8n.io/workflows/2834
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Overview  
  This automated workflow fetches Upwork job postings using Apify, removes duplicate job listings via MongoDB, and sends new job opportunities to Slack.  

  Key Features:  
  Automated job retrieval** from Upwork via Apify API  
  Duplicate filtering** using MongoDB to store only unique jobs  
  Slack notifications** for new job postings  
  Runs every 20 minutes** during working hours (9 AM - 5 PM)  

  This workflow requires an active Apify subscription to function, as it uses the Apify Upwork API to fetch job listings. 

  Who is This For?  
  This workflow is ideal for:  
  Freelancers looking to track Upwork jobs in real time  
  Recruiters automating job collection for analytics  
  Developers who want to integrate Upwork job data into their applications  

  What Problem Does This Solve?  
  Manually checking Upwork for jobs is time-consuming and inefficient. This workflow:  
  Automates job discovery based on your keywords  
  Filters out duplicate listings, ensuring only new jobs are stored  
  Notifies you on Slack when new jobs appear  

  How the Workflow Works  

  1. Schedule Trigger (Every 20 Minutes)  
  Triggers the workflow at 20-minute intervals  
  Ensures job searches are only executed during working hours (9 AM - 5 PM)  

  2. Query Upwork for Jobs  
  Uses Apify API to scrape Upwork job posts for specific keywords (e.g., "n8n", "Python")  

  3. Find Existing Jobs in MongoDB  
  Searches MongoDB to check if a job (based on title and budget) already exists  

  4. Filter Out Duplicate Jobs  
  The Merge Node compares Upwork jobs with MongoDB data  
  The IF Node filters out jobs that are already stored in the database  

  5. Save Only New Jobs in MongoDB  
  The Insert Node adds only new job listings to the MongoDB collection  

  6. Send a Slack Notification  
  If a new job is found, a Slack message is sent with job details  

  Setup Guide  

  Required API Keys  
  Upwork Scraper (Apify Token) – Get your token from Apify
  MongoDB Credentials – Set up MongoDB in n8n using your connection string  
  Slack API Token – Connect Slack to n8n and set the channel ID (default: #general)  

  Configuration Steps  
  Modify search keywords in the 'Assign Parameters' node (startUrls)  
  Adjust the Working Hours in the 'If Working Hours' node  
  Set your Slack channel in the Slack node  
  Ensure MongoDB is connected properly  
  Adjust the 'If Working Hours' node to match your timezone and hours, or remove it altogether to receive notifications and updates constantly.

  How to Customize the Workflow  
  Change keywords: update the startUrls in the 'Assign Parameters' node to track different job categories  
  Change 'If Working Hours': Modify conditions in the IF Node to filter times based on your needs  
  Modify Slack Notifications: Adjust the Slack message format to include additional job details  

  Why Use This Workflow?  
  Automated job tracking without manual searches  
  Prevents duplicate entries in MongoDB  
  Instant Slack notifications for new job opportunities  
  Customizable – adapt the workflow to different job categories  

  Next Steps  
  Run the workflow and test with a small set of keywords  
  Expand job categories for better coverage  
  Enhance notifications by integrating Telegram, Email, or a dashboard  

  This workflow ensures real-time job tracking, prevents duplicates, and keeps you updated effortlessly.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.mongoDb","n8n-nodes-base.merge","n8n-nodes-base.mongoDb","n8n-nodes-base.stickyNote","n8n-nodes-base.slack"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","web-scraping","notifications","data-storage","integration"],"complexity":"medium","use_cases":["Real-time Upwork job alerts for freelancers","Automated job collection for recruiters/TA teams","Feeding Upwork job data into analytics or apps","Preventing duplicate job entries for clean datasets","Custom keyword-based job monitoring"],"estimated_setup_minutes":20,"required_services":["Apify (Upwork scraper) - requires active subscription/token","MongoDB (database / connection string)","Slack (bot token and channel ID)","n8n (workflow automation platform)"],"key_features":["Periodic scraping of Upwork via Apify","Duplicate detection and storage in MongoDB","Slack notifications for newly found jobs","Scheduled runs with working-hours filter","Customizable search keywords and message format"],"target_audience":["Freelancers","Recruiters / talent acquisition teams","Developers integrating job data into apps/dashboards"]}


name: Docker Registry Cleanup Workflow
author_name: Muzaffer AKYIL
author_username: victorious
categories: []
created_at: 2025-02-01T23:26:03.445Z
updated_at: 2025-02-01T23:26:03.445Z
url: https://n8n.io/workflows/2835
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:32
description:
  Docker Registry Cleanup Template

  This template is designed to automatically clean up old image tags in the Docker registry and perform garbage collection.

  Features
  List all images in the registry
  Preserve the last 10 tags for each image (latest tag is always preserved)
  Delete old tags
  Email notification for Successful/Excused cancellation
  Registry garbage collection automation
  Failure notification in error conditions

  Prerequisites
  Docker Registry v2 API access
  Basic Authentication credentials
  SMTP email settings (for notifications)
  SSH node installed on n8n (for garbage collection)

  Installation

  1. Identity Information
  Add the following credentials in n8n:
  HTTP Basic Auth**: For Registry access
  SSH Private Key**: For Garbage collection command
  Email SMTP**: For notifications

  2. Set Variables
  Replace your-registry-url with your actual registry URL on all nodes:
  ‘url": ‘https://your.registry.com/v2/_catalog’.

  Customisation
  Retention Policy: 
  Set the number of tags to be retained by changing the slice(0, 10) value in the  Identify Tags to Remove node
  Schedule:
  Change the frequency of operation at the Trigger node
  Notification Content: 
  Customise email templates according to your needs

  Notes
  Check DELETE operations before running in a test environment

  Make sure that the registry is not in read-only mode

  The registry may need to be put into maintenance mode for garbage collection

  Step Details:
  Retrieving image information:** The workflow starts by fetching a list of images and their associated tags from the Docker registry.
  Filtering and sorting:** The retrieved tags are then filtered and sorted based on specific criteria, such as creation date and tag name.
  Deleting old tags:** The workflow identifies old or unused tags and attempts to delete them from the registry.
  Sending notifications:** The workflow sends email notifications to inform the user about the status of the cleanup process, including any errors or successes.
  Executing additional cleanup tasks:** Finally, the workflow executes an SSH command on the Docker registry server to perform additional cleanup tasks, such as garbage collection.

  TL;DR
  In summary, this n8n template provides a robust and automated solution for managing and cleaning up Docker registries. By regularly running this workflow, users can ensure that their registry remains organized and efficient, and avoid running out of storage space.- 

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.emailSend","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.sort","n8n-nodes-base.emailSend","n8n-nodes-base.ssh"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","devops","integration","maintenance"],"complexity":"medium","use_cases":["Automated cleanup of old Docker image tags","Enforcing image retention policies","Scheduled garbage collection and storage reclamation","Registry maintenance and housekeeping","Notification on success/failure of cleanup jobs"],"estimated_setup_minutes":30,"required_services":["Docker Registry (v2) API access","SMTP email server for notifications","SSH access to the registry host (for garbage collection)","n8n with SSH node installed"],"key_features":["Lists images and tags from the Docker registry","Preserves the last N tags per image (default: last 10, latest always preserved)","Identifies and deletes older tags via registry API","Automates registry garbage collection over SSH","Email notifications for success, cancellation, and errors (SMTP based, customizable templates and content)"],"target_audience":["DevOps engineers","Site Reliability Engineers (SREs)","Platform/Infrastructure engineers managing container registries"]}


