# templates

name: Create Dynamic Workflows Programmatically via Webhooks & n8n API
author_name: Mauricio Perera
author_username: rckflr
categories: []
created_at: 2025-06-01T01:54:12.908Z
updated_at: 2025-06-01T01:54:12.908Z
url: https://n8n.io/workflows/4544
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Overview

  This workflow exposes an HTTP endpoint (webhook) that accepts a JSON definition of an n8n workflow, validates it, and—if everything is correct—dynamically creates that workflow in the n8n instance via its internal API. If any validation fails or the API call encounters an error, an explanatory message with details is returned.

  Workflow Diagram

  Webhook
     │
     ▼
  Validate JSON ── fails validation ──► Validation Error
     │
     └─ passes ─► Validation Successful?
                             │
                             ├─ true ─► Create Workflow ──► API Successful? ──► Success Response
                             │                                 │
                             │                                 └─ false ─► API Error
                             └─ false ─► Validation Error

  Step-by-Step Details

  1. Webhook

  Type**: Webhook (POST)
  Path**: /webhook/create-workflow
  Purpose**: Expose a URL to receive a JSON definition of a workflow.
  Expected Input**: JSON containing the main workflow fields (name, nodes, connections, settings).

  2. Validate JSON

  Type**: Code Node (JavaScript)
  Validations Performed**:

    Ensure that payload exists and contains both name and nodes.
    Verify that nodes is an array with at least one item.
    Check that each node includes the required fields: id, name, type, position.
    If missing, initialize connections, settings, parameters, and typeVersion.
  Output if Error**:

      { "success": false, "message": "&lt;error description&gt;" }
    Output if Valid**:

      {
      "success": true,
      "apiWorkflow": {
        "name": payload.name,
        "nodes": payload.nodes,
        "connections": payload.connections,
        "settings": payload.settings
      }
    }
  3. Validation Successful?

  Type**: IF Node
  Condition**: $json.success === true
  Branches**:

    true: proceed to Create Workflow
    false: route to Validation Error

  4. Create Workflow

  Type**: HTTP Request (POST)
  URL**: http://127.0.0.1:5678/api/v1/workflows
  Authentication**: Header Auth with internal credentials
  Body**: The apiWorkflow object generated earlier
  Options**: continueOnFail: true (to handle failures in the next IF)

  5. API Successful?

  Type**: IF Node
  Condition**: $response.statusCode &lt;= 299
  Branches**:

    true: proceed to Success Response
    false: route to API Error

  6. Success Response

  Type**: SET Node
  Output**:

      {
      "success": "true",
      "message": "Workflow created successfully",
      "workflowId": "{{ $json.data[0].id }}",
      "workflowName": "{{ $json.data[0].name }}",
      "createdAt": "{{ $json.data[0].createdAt }}",
      "url": "http://localhost:5678/workflow/{{ $json.data[0].id }}"
    }
  7. API Error

  Type**: SET Node
  Output**:

      {
      "success": "false",
      "message": "Error creating workflow",
      "error": "{{ JSON.stringify($json) }}",
      "statusCode": "{{ $response.statusCode }}"
    }
  8. Validation Error

  Type**: SET Node
  Output**:

      { "success": false, "message": "{{ $json.message }}" }
  Example Webhook Request

  curl --location --request POST 'http://localhost:5678/webhook/create-workflow' \
  --header 'Content-Type: application/json' \
  --data-raw '{
    "name": "My Dynamic Workflow",
    "nodes": [
      {
        "id": "start-node",
        "name": "Start",
        "type": "n8n-nodes-base.manualTrigger",
        "typeVersion": 1,
        "position": [100, 100],
        "parameters": {}
      },
      {
        "id": "set-node",
        "name": "Set",
        "type": "n8n-nodes-base.set",
        "typeVersion": 1,
        "position": [300, 100],
        "parameters": {
          "values": {
            "string": [
              { "name": "message", "value": "Hello from a webhook-created workflow!" }
            ]
          }
        }
      }
    ],
    "connections": {
      "Start": {
        "main": [
          [ { "node": "Set", "type": "main", "index": 0 } ]
        ]
      }
    },
    "settings": {}
  }'

  Expected Success Response

  {
    "success": "true",
    "message": "Workflow created successfully",
    "workflowId": "abcdef1234567890",
    "workflowName": "My Dynamic Workflow",
    "createdAt": "2025-05-31T12:34:56.789Z",
    "url": "http://localhost:5678/workflow/abcdef1234567890"
  }

  Validation Error Response

  {
    "success": false,
    "message": "The 'name' field is required in the workflow"
  }

  API Error Response

  {
    "success": "false",
    "message": "Error creating workflow",
    "error": "{ ...full API response details... }",
    "statusCode": 401
  }

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

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","developer tools","workflow management"],"complexity":"medium","use_cases":["Programmatically create and deploy n8n workflows","Dynamic workflow provisioning from external systems","Automating onboarding or tenant-specific workflow setup","CI/CD or automation pipelines that generate workflows","Validating workflow definitions before creation"],"estimated_setup_minutes":20,"required_services":["n8n instance (internal API at /api/v1/workflows)","HTTP/Webhook access to the n8n instance","Header authentication credentials for n8n internal API"],"key_features":["Exposes a webhook endpoint to receive workflow JSON","Validates incoming workflow definitions (name, nodes, required fields)","Initializes missing node fields (connections, settings, parameters, typeVersion)","Creates workflows dynamically via internal n8n API","Error handling with clear Validation / API error responses and status codes"],"target_audience":["Developers","Platform / DevOps engineers","Automation engineers / architects"]}


name: AI-Powered Employee Database Management via Telegram using OpenAI and Airtable
author_name: Billy Christi
author_username: billy
categories: []
created_at: 2025-06-01T02:01:03.787Z
updated_at: 2025-06-01T02:01:03.787Z
url: https://n8n.io/workflows/4545
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this for?

  This workflow is perfect for:

  HR professionals** seeking to automate employee and department management
  Startups and SMBs** that want an AI-powered HR assistant on Telegram
  Internal operations teams** that want to simplify onboarding and employee data tracking

  What problem is this workflow solving?

  Managing employee databases manually is error-prone and inefficient—especially for growing teams. This workflow solves that by:

  Enabling natural language-based HR operations directly through Telegram
  Automating the creation, retrieval, and deletion of employee records in Airtable
  Dynamically managing related data such as departments and job titles
  Handling data consistency and linking across relational tables automatically
  Providing a conversational interface backed by OpenAI for smart decision-making

  What this workflow does

  Using Telegram as the interface and Airtable as the backend database, this intelligent HR workflow allows users to:

  Chat in natural language (e.g. “Show me all employees” or “Create employee: Sarah, Marketing…”)
  Interpret and route requests via an AI Agent that acts as the orchestrator
  Query employee, department, and job title data from Airtable
  Create or update records as needed:

     Add new departments and job titles automatically if they don’t exist
     Create new employees and link them to the correct department and job title
  Delete employees based on ID
  Respond directly in Telegram, providing user-friendly feedback

  Setup
  View & Copy the Airtable base here:
     👉 Employee Database Management – Airtable Base Template

  Telegram Bot: Set up a Telegram bot and connect it to the Telegram Trigger node

  Airtable: Prepare three Airtable tables:

     Employees with links to Departments and Job Titles
     Departments with Name & Description
     Job Titles with Title & Description

  Connect your Airtable API key and base/table IDs into the appropriate Airtable nodes

  Add your OpenAI API key to the AI Agent nodes

  Deploy both workflows: the main chatbot workflow and the employee creation sub-workflow

  Test with sample messages like:

     “Create employee: John Doe, john@company.com, Engineering, Software Engineer”
     “Remove employee ID rec123xyz”

  How to customize this workflow to your needs

  Switch databases**: Replace Airtable with Notion, PostgreSQL, or Google Sheets if desired
  Enhance security**: Add authentication and validation before allowing deletion
  Add approval flows**: Integrate Telegram button-based approvals for sensitive actions
  Multi-language support**: Expand system prompts to support multiple languages
  Add logging**: Store every user action in a log table for auditability
  Expand capabilities**: Integrate payroll, time tracking, or Slack notifications

  Extra Tips

  This is a two-workflow setup. Make sure the sub-workflow is deployed and accessible from the main agent.
  Use Simple Memory per chat ID to preserve context across user queries.
  You can expand the orchestration logic by adding more tools to the main agent—such as “Get active employees only” or “List employees by job title.”

  Contact me for consulting and support:   
  📧 billychartanto@gmail.com

nodes_used:
  ["n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.telegram","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7088 characters]

metadata_json:
  {"categories":["automation","integration","data management","chatbot/AI"],"complexity":"complex","use_cases":["Natural-language CRUD for employee records","Automated onboarding and employee data entry","Querying and listing employees, departments, and job titles via chat","Maintaining relational data consistency across tables","Building an AI-powered HR assistant in Telegram"],"estimated_setup_minutes":45,"required_services":["Telegram Bot API","Airtable (base & API)","OpenAI API (ChatGPT)","n8n (workflow runner)"],"key_features":["Natural-language conversational interface via Telegram","AI Agent orchestration with OpenAI to interpret and route requests","Create, update, retrieve, and delete employee records in Airtable","Automatic creation/linking of Departments and Job Titles if missing","Sub-workflow for employee creation called from main agent workflow (two-workflow setup)"],"target_audience":["HR professionals","Startups and SMBs","Internal operations teams / people ops owners"]}


name: Auto-Generate & Publish SEO Blog Posts to WordPress with OpenRouter & Runware
author_name: Khairul Muhtadin
author_username: khmuhtadin
categories: []
created_at: 2025-06-01T03:24:24.440Z
updated_at: 2025-06-01T03:24:24.440Z
url: https://n8n.io/workflows/4546
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automate Blog Creation and Publishing with Ultra-Low Cost AI

  This n8n workflow — BlogBlizt: Runaware Edition — automates the creation, enrichment, and publishing of SEO-optimized blog posts for WordPress using entirely free OpenRouter AI models for text and metadata generation, plus ultra-low-cost Runware AI for realistic featured images (as low as $0.0016 per image).

  It’s triggered every 3 hours or manually via Telegram. Each run generates 1,500–3,500-word articles on trending topics in Technology, AI, Tech Facts, History, or Tips — all complete with catchy titles, slugs, meta descriptions, and visuals.

  💰 Why This Is So Cost-Efficient

  ✅ Free metadata + article generation (OpenRouter free-tier models)
  💸 Only cost is for image generation via Runware:
    Image generation: ~$0.0016/image (the cheapest model could be $0,00065)

  This is ideal for scale — generating dozens of high-quality blog posts with minimal expense.

  ⚡ Who Is This For?

  Bloggers who want high-quality content without writing
  SEO marketers seeking fresh, long-form articles
  WordPress site owners automating publishing workflows
  Growth hackers targeting high-volume content generation

  ❓ What Problem Does It Solve?

  Creating SEO-rich blog content is time-consuming, expensive, and requires expertise. BlogBlizt eliminates this friction by using free models (OpenRouter) to generate text and metadata — making this workflow nearly free to operate, with the only cost being the featured image (~$0.0016/image).

  🔧 What This Workflow Does

  ⏱ Triggers: Every 3 hours (or via Telegram command: generate)
  🧠 Generates Metadata: Title, slug, category, focus keyphrase, and meta description using OpenRouter
  ✍️ Writes SEO Article: 1,500–3,500 words with headings, H2/H3s, CTA, and outbound links
  🖼️ Creates Image: Generates realistic 1024×1024 blog image with Runware AI
  📤 Publishes to WordPress: Auto-publishes with category ID, featured image, and Yoast SEO fields
  📣 Notifies: Sends publish link to Discord and Telegram (optional)

  🔐 Setup Instructions

  Import .json into n8n
  Add credentials:
    OpenRouter API (free LLMs like LLaMA-3, Nemotron)
    Runware API key (get it here)
    (Optional) Telegram Bot + Discord Webhook
  Test by sending the word generate via Telegram

  🧩 Pre-Requirements

  Self-hosted or cloud-based n8n instance
  WordPress with category IDs configured:
    Technology → 3
    AI → 4
    Tech Fact → 7
    Tech History → 8
    Tech Tips → 9
  OpenRouter API Key (free)
  Runware API Key (for images)
  WordPress user ID (admin, default is 1)
  Telegram bot (optional)
  Discord webhook (optional)

  🛠️ Customize It Further

  Change writing tone or prompt in ✍️ Compose SEO Article Content node
  Cofigure the category
  Swap free LLMs (Claude, Gemini, GPT, etc.) in OpenRouter settings
  Adjust schedule (every 6h, daily, etc.)
  Extend alerts (email, Slack, Notion)

  🧠 Nodes Used

  Schedule Trigger
  Telegram Trigger
  @n8n/n8n-nodes-langchain (OpenRouter)
  WordPress Node
  HTTP Request (Runware API)
  Discord Webhook
  Sticky Notes (Documentation inside canvas)

  📞 Support

  Created by: Khaisa Studio  
  Tag: youtube, summarizer, telegram, openai  
  Category: AI Automation, Video Tools
  Need a custom? contact me on LinkedIn or Web

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.wordpress","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.discord","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","content creation","SEO","integration","AI"],"complexity":"complex","use_cases":["Automated generation of long-form SEO blog posts","High-volume content publishing to WordPress","Low-cost AI-driven content & image creation","Content marketing and growth-hacking workflows","Automated notifications after publishing (Telegram/Discord)"],"estimated_setup_minutes":30,"required_services":["n8n instance (self-hosted or cloud)","OpenRouter API key (free LLM models)","Runware API key (image generation)","WordPress site with REST API access (and category IDs)","Telegram Bot (optional trigger)","Discord webhook (optional notifications)"],"key_features":["Scheduled or manual (Telegram) trigger every 3 hours","Generates title, slug, category, focus keyphrase, meta description","Writes 1,500–3,500 word SEO-optimized articles with H2/H3, CTA and outbound links","Creates realistic 1024×1024 featured images via Runware (very low cost)","Publishes posts to WordPress including Yoast SEO fields and featured image attachment ID handling"],"target_audience":["Bloggers and WordPress site owners","SEO marketers and content teams","Growth hackers and scrappy publishers aiming for scale-level content output at low cost"]}


name: Documentation Lookup AI Agent using Context7 and Gemini
author_name: Jez
author_username: jez
categories: []
created_at: 2025-06-01T03:32:24.786Z
updated_at: 2025-06-01T03:32:24.786Z
url: https://n8n.io/workflows/4547
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n. 

  This workflow demonstrates how to build and expose a sophisticated n8n AI Agent as a single, callable tool using the Multi-Agent Collaboration Protocol (MCP). It allows external clients or other AI systems to easily query software library documentation via Context7, without needing to manage the underlying tool orchestration or complex conversational logic.

  Core Idea:
  Instead of building complex agentic loops on the client-side (e.g., in Python, a VS Code extension, or another AI development environment), this workflow offloads the entire agent's reasoning and tool-use process to n8n. The client simply sends a natural language query (like "How do I use Flexbox in Tailwind CSS?") to an SSE endpoint, and the n8n agent handles the rest.

  Key Features & How It Works:

  Public MCP Endpoint:
      The main workflow uses the Context7 MCP Server Trigger node to create an SSE endpoint. This makes the agent accessible to any MCP-compatible client.
      The path for the endpoint is kept long and random for basic 'security by obscurity'.
  Tool Workflow as an Interface:
      A Tool Workflow node (named call_context7_ai_agent in this example) is connected to the MCP Server Trigger. This node defines the single "tool" that external clients will see and call.
  Dedicated AI Agent Sub-Workflow:
      The call_context7_ai_agent tool invokes a separate sub-workflow which contains the actual AI logic.
      This sub-workflow starts with a Context7 Workflow Start node to receive the user's query.
      A Context7 AI Agent node (using Google Gemini in this example) is the brain, equipped with:
          A system prompt to guide its behavior.
          Simple Memory to retain context for each execution (using {{ $execution.id }} as the session key).
          Two specialized Context7 MCP client tools:
              context7-resolve-library-id: To convert library names (e.g., 'Next.js') into Context7-specific IDs.
              context7-get-library-docs: To fetch documentation using the resolved ID, with options for specific topics and token limits.
  Seamless Tool Use: The AI Agent autonomously decides when and how to use the resolve-library-id and get-library-docs tools based on the user's query, handling the multi-step process internally.

  Benefits of This Approach:

  Simplified Client Integration:** Clients interact with a single, powerful tool, sending a simple query.
  Reduced Client-Side Token Consumption:** The detailed prompts, tool descriptions, and conversational turns are managed server-side by n8n, saving tokens on the client (especially useful if the client is another LLM).
  Centralized Agent Management:** Update your agent's capabilities, tools, or LLM model within n8n without any changes needed on the client side.
  Modularity for Agentic Systems:** Perfect for building complex, multi-agent systems where this n8n workflow can act as a specialized "expert" agent callable by others (e.g., from environments like Smithery).
  Cost-Effective:** By using a potentially less expensive model (like Gemini Flash) for the agent's orchestration and leveraging the free tier or efficient pricing of services like Context7, you can build powerful solutions economically.

  Use Cases:

  Providing an intelligent documentation lookup service for coding assistants or IDE extensions.
  Creating specialized AI "micro-agents" that can be consumed by larger AI applications.
  Building internal knowledge base query systems accessible via a simple API-like interface.

  Setup:

  Ensure you have the necessary n8n credentials for Google Gemini (or your chosen LLM) and the Context7 MCP client tools.
  The Path in the Context7 MCP Server Trigger node should be unique and secure.
  Clients connect to the "Production URL" (SSE endpoint) provided by the trigger node.

  This workflow is a great example of how n8n can serve as a powerful backend for building and deploying modular AI agents.

  I've made a video to try and explain this a bit too https://www.youtube.com/watch?v=dudvmyp7Pyg

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-mcp.mcpClientTool","n8n-nodes-mcp.mcpClientTool","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","knowledge_retrieval","ai"],"complexity":"complex","use_cases":["Intelligent documentation lookup for coding assistants or IDE extensions","Expose a single callable AI tool for other agents or services (MCP-compatible clients)","Build specialized AI micro-agents for larger multi-agent systems","Internal knowledge-base or engineering documentation query service"],"estimated_setup_minutes":45,"required_services":["Self-hosted n8n","Context7 (MCP server/client and documentation APIs)","Google Gemini (or another LLM) credentials"],"key_features":["Public MCP SSE endpoint (Context7 MCP Server Trigger)","Single Tool Workflow interface that clients call","Dedicated sub-workflow implementing the AI agent logic","Context7 AI Agent with system prompt and per-execution memory","Two Context7 MCP client tools: resolve-library-id and get-library-docs for document retrieval automation and token bounds control (topic + token limits) 
  (autonomous tool use) 
  "],"target_audience":["Developers building IDE integrations or coding assistants","Platform/DevOps engineers deploying self-hosted automation","AI/ML engineers and architects building agentic systems or micro-agents to be consumed by other agents/apps"]}


name: Manage User Authentication with Telegram, Redis Cache and Google Sheets
author_name: Archit Jain
author_username: architjn
categories: []
created_at: 2025-06-01T05:32:09.661Z
updated_at: 2025-06-01T05:32:09.661Z
url: https://n8n.io/workflows/4548
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  How It Works

  Telegram bot listens for messages and uses chat ID as the unique user identifier.
  Checks Google Sheets for existing users; registers new users if not found.
  Caches active sessions in Redis for fast lookup and login-state handling.
  Stores and retrieves user data (UserID, Name, Telegram Chat ID) in Google Sheets.
  Can be triggered by other n8n workflows to fetch or update user/session data.

  Setup Steps

  Obtain Telegram Bot token, Google Sheets “Users” sheet with API credentials, and a Redis instance.
  Import the n8n workflow and configure Redis, Telegram Bot, and Google Sheets credentials.
  Expect 30–45 minutes to complete workflow import, Redis setup, and API wiring.
  Refer to sticky notes inside the workflow for exact field mappings and node configurations.

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.merge","n8n-nodes-base.redis","n8n-nodes-base.switch","n8n-nodes-base.redis","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.merge","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","integration","authentication","caching"],"complexity":"medium","use_cases":["Telegram-based user registration and authentication","Session management and fast login-state lookup","Storing and syncing user data in Google Sheets","Exposing user/session lookup to other n8n workflows"],"estimated_setup_minutes":40,"required_services":["Telegram Bot API (bot token)","Google Sheets API (service account / API credentials)","Redis instance","n8n"],"key_features":["Telegram bot listens and identifies users by chat ID","Checks and registers users in Google Sheets","Caches active sessions in Redis for fast lookup","Nodes to fetch/update user and session data from other workflows","Configurable field mappings via sticky notes"],"target_audience":["developers","automation engineers","IT/ops teams managing light-weight auth flows"]}


name: AI-Powered RAG Document Processing & Chatbot with Google Drive, Supabase, OpenAI
author_name: Billy Christi
author_username: billy
categories: []
created_at: 2025-06-01T06:44:01.484Z
updated_at: 2025-06-01T06:44:01.484Z
url: https://n8n.io/workflows/4551
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Who is this for?

  This workflow is perfect for:

  Businesses and teams who need an automated solution to organize, analyze, and retrieve insights from their internal documents.
  Researchers who want to quickly analyze and query large collections of research papers, reports, or datasets.
  Customer support teams looking to streamline access to product documentation and support resources.
  Legal and compliance professionals needing to reference and query legal documents with confidence.
  AI enthusiasts and developers wanting to implement Retrieval-Augmented Generation (RAG) systems without starting from scratch.

  What problem is this workflow solving?

  Manually organizing, processing, and searching through documents can be time-consuming, error-prone, and inefficient. This workflow solves that by:

  Automating document processing** from Google Drive, supporting multiple formats like PDFs, CSVs, and Google Docs.
  Extracting, chunking, and enhancing document text**, preserving context and improving AI comprehension.
  Storing vector embeddings** in a secure, scalable Supabase vector database, enabling semantic search and retrieval.
  Providing an interactive AI chat interface** that allows users to ask natural language questions and get precise, document-based answers.

  This means teams can quickly access relevant insights from their document repositories—boosting productivity and ensuring accurate information retrieval.

  Key Features

  🚀 End-to-End Document Processing: From Google Drive upload detection to vector embedding and storage.
  🔍 Semantic Search & Retrieval: Users can ask complex, natural-language questions and receive contextually relevant answers.
  🤖 AI-Powered Summaries & Metadata: Automatically generates document titles and summaries using Google Gemini AI.
  📝 Smart Chunking & Contextual Enhancement: Breaks documents into smart chunks with overlap, preserving context and table integrity.
  🔐 Secure & Scalable Vector Database: Stores and retrieves embeddings in a Supabase vector store for fast, reliable searches.
  💬 Conversational AI Interface: Uses OpenAI to power natural, accurate, and cost-effective AI chat interactions.

  How does this workflow work?

  Monitors Google Drive for new files
  Extracts text from PDFs and CSVs (or Google Docs auto-converted)
  Splits text into context-preserving chunks
  Enhances chunk quality and stores embeddings in Supabase
  Enables natural language search and AI-powered chat interactions with the stored documents

  Typical Use Cases

  📚 Corporate Knowledge Base
  🔬 Research Paper Analysis
  📞 Customer Support Document Query
  ⚖️ Legal Document Review and Analysis
  🔍 Internal Team Documentation Search

  Why You’ll Love It

  This workflow lets you build a scalable, searchable, and AI-powered document system—without needing to write complex code or manage multiple systems. With this, you can:

  Stay organized with automated document processing.
  Deliver faster, more accurate answers to user queries.
  Reduce manual work and improve productivity.
  Gain a competitive edge with cutting-edge AI search capabilities.

  Setup Requirements

  An n8n instance with Google Drive, Supabase, OpenAI, and Gemini credentials configured.
  Access to a Supabase vector store for storing document embeddings.
  Configurable chunk size, overlap, and processing limits (default: 1000 characters per chunk, 20 chunks max).

  Contact me for consulting and support:   
  📧 billychartanto@gmail.com

nodes_used:
  ["n8n-nodes-base.splitInBatches","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","n8n-nodes-base.googleDriveTrigger","@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.limit","n8n-nodes-base.summarize","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML","knowledge management"],"complexity":"complex","use_cases":["Corporate knowledge base and internal documentation search","Research paper ingestion and analysis","Customer support document retrieval and QA","Legal and compliance document review and querying","Building a RAG-powered chatbot for internal files"],"estimated_setup_minutes":45,"required_services":["n8n instance","Google Drive (for source files)","Supabase (vector store)","OpenAI (chat + embeddings)","Google Gemini (AI summaries/metadata)"],"key_features":["Automated ingestion from Google Drive (PDF, CSV, Google Docs)","Smart chunking with overlap and table-aware processing","Embedding generation and storage in Supabase vector DB","Semantic search / retrieval for natural-language queries","AI-generated document titles and summaries (Gemini)"],"target_audience":["Product and engineering teams","Researchers and analysts","Customer support teams and documentation owners\",\"Legal and compliance professionals\",\"AI developers and enthusiasts"]}


name: Index Documents from Google Drive to Pinecone with OpenAI Embeddings for RAG
author_name: Automate With Marc
author_username: marconi
categories: []
created_at: 2025-06-01T07:53:00.731Z
updated_at: 2025-06-01T07:53:00.731Z
url: https://n8n.io/workflows/4552
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧠 Google Drive Upload Trigger → Pinecone Vector Upsert for Document Indexing
  Category: AI & LLM / Document Indexing
  Level: Intermediate
  Tags: Google Drive, Pinecone, OpenAI, Embeddings, Vector Store, LangChain, RAG

  📄 What This Workflow Does
  This workflow watches a specific Google Drive folder and automatically uploads any newly added document to a Pinecone vector database — complete with OpenAI-generated embeddings.

  Perfect for setting up retrieval-augmented generation (RAG) pipelines, semantic search, or document Q&A systems. Once configured, your knowledge base stays up-to-date with zero manual effort.

  Watch Full Step By Stey Tutorial Video Here:
  https://www.youtube.com/@Automatewithmarc

  🔧 How It Works
  📁 Google Drive Trigger
  Watches a specific folder and triggers when new documents are uploaded.

  🔍 Google Drive File Search & Download
  Finds and fetches all files in the folder.

  🔄 Loop Over Each File
  Handles batch processing for multiple files.

  📃 Document Loader
  Parses each file as binary and applies custom metadata like document type.

  ✂️ Text Splitter
  Breaks content into manageable chunks for embedding (e.g., 600 characters, 60 overlap).

  🧠 OpenAI Embeddings
  Generates vector embeddings using OpenAI.

  📦 Pinecone Vector Store
  Inserts/upserts documents into a specific Pinecone namespace for search-ready indexing.

  🧠 Why This is Useful
  This is a production-grade setup for:

  Building vector search tools over internal docs

  Feeding up-to-date data into RAG agents or chatbots

  Auto-tagging and chunking files for scalable AI workflows

  Whether you’re indexing course outlines, SOPs, or technical docs — this automation keeps your vector store fresh and organized.

  🪜 Setup Instructions
  Connect your Google Drive, OpenAI, and Pinecone accounts.

  Specify the Google Drive folder to monitor.

  Customize metadata, chunk size, or vector namespace as needed.

  Activate the workflow and drop a file into the folder — magic happens behind the scenes.

  📌 Notes
  Works best with PDFs or text-based documents.

  You can swap out OpenAI with other embedding models if needed.

  Consider adding notifications or logging (e.g., via Slack or email) for better observability.

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI & LLM","Document Indexing","Automation","Vector Databases","Integration"],"complexity":"medium","use_cases":["Automated document indexing for retrieval-augmented generation (RAG)","Semantic search over internal documents","Document Q&A and knowledge base building","Keeping vector stores up-to-date with new uploads","Preprocessing and chunking documents for embeddings"],"estimated_setup_minutes":30,"required_services":["Google Drive","OpenAI (Embeddings API)","Pinecone"],"key_features":["Folder watch trigger for new Google Drive uploads","Search & download files from a Drive folder","Batch processing / loop over multiple files","Document loader that extracts binary content and metadata","Recursive text splitter (configurable chunk size & overlap) for chunks suitable for embeddings\",\"OpenAI embeddings generation and mapping to chunks\",\"Upsert vectors into a Pinecone namespace for search-ready indexing\",\"Supports PDFs and text-based documents; model swap possible for embeddings"],"target_audience":["Developers","ML engineers / Data engineers","AI & LLM builders working on RAG systems or chatbots\",\"Product teams building internal search or knowledge tooling"]}


name: Generate Guerrilla Marketing Campaign Plans with AI Swarm Intelligence
author_name: Sina
author_username: sina2266
categories: []
created_at: 2025-06-01T11:31:39.415Z
updated_at: 2025-06-01T11:31:39.415Z
url: https://n8n.io/workflows/4553
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧠 Who is this for?
  Startup founders designing creative growth strategies  
  Marketing teams seeking low-cost, high-impact campaigns  
  Consultants and agencies needing fast guerrilla plans  
  Creators exploring AI-powered content and campaigns  

  ❓ What problem does this workflow solve?
  Building a full guerrilla marketing strategy usually takes hours of brainstorming, validation, and formatting. This template does all of that in minutes using a swarm of AI agents, from idea generation to KPIs, and even kills bad ideas before you waste time on them.

  ⚙️ What this workflow does
  Starts with a chat input where you describe your business or idea  
  A “Swarm Intelligence” loop:  
    One AI agent generates guerrilla ideas  
    Another agent critically validates the idea and gives honest feedback  
    If the idea is weak, it asks for a new one  
  If accepted, the swarm continues with 16 AI specialists generating:  
    🎯 Objectives  
    🧍‍♂️ Personas  
    🎤 Messaging  
    🧨 Tactics  
    📢 Channels  
    🧮 Budget  
    📊 KPIs  
    📋 Risk plan and more  
  Merges all chapters into a final Markdown file  
  Lets you download the campaign in seconds  

  🛠️ Setup
  Import the workflow to your n8n instance  
  (Optional) Configure your LLM (OpenAI or Ollama) in the “OpenAI Chat Model” node   
  Type your business idea (e.g., “Luxury dog collar brand for Instagram dads”)  
  Wait for flow completion
  Download the final marketing plan file  

  🤖 LLM Flexibility (Choose Your Model)
  Supports any LLM via LangChain:  
    Ollama (LLaMA 3.1, Mistral, DeepSeek)  
    OpenAI (GPT-4, GPT-3.5)  
  To switch models, just replace the “Language Model” node, no other logic needs updating  



  📌 Notes
  Output is professional and ready-to-pitch  
  Built-in pessimistic validator filters out bad ideas before wasting time  

  📩 Need help?
  Email: sinamirshafiee@gmail.com  
  Happy to support setup or customization!

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.convertToFile"]

workflow_json_compressed:
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metadata_json:
  {"categories":["marketing","automation","ai","content generation","strategy"],"complexity":"medium","use_cases":["Generate full guerrilla marketing campaign plans quickly","AI-driven idea generation and pessimistic validation","Create pitch-ready Markdown campaign documents","Rapidly produce campaign components: objectives, personas, messaging, tactics, channels, budget, KPIs, risk plan"],"estimated_setup_minutes":10,"required_services":["n8n instance","LLM provider (OpenAI or Ollama or any LangChain-compatible model)","LangChain connector (used within the workflow)"],"key_features":["Swarm intelligence loop with iterative idea generation and critical validation","16 specialist AI agents producing campaign chapters (objectives, personas, messaging, tactics, channels, budget, KPIs, risk plan, etc.)","Pessimistic validator that filters out weak ideas before proceeding","Merges all chapters into a single Markdown file for download","Model-agnostic LLM node — easy swap between OpenAI/Ollama without workflow changes"],"target_audience":["Startup founders","Marketing teams and growth marketers","Consultants and agencies creating client strategies quickly」「Creators and solo entrepreneurs exploring AI-powered campaigns"]}


name: Monitor Google Shopping Prices with Bright Data & Email Alerts
author_name: Dvir Sharon
author_username: dvirsharon
categories: []
created_at: 2025-06-01T11:49:47.000Z
updated_at: 2025-06-01T11:49:47.000Z
url: https://n8n.io/workflows/4554
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  🛒 Monitor Google Shopping Prices with Bright Data & Email Alerts
  This template requires a self-hosted n8n instance to run.

  A comprehensive n8n automation that monitors product prices daily using Bright Data's Google Shopping dataset and sends smart email alerts when price conditions are met.

  📋 Overview
  This workflow provides an automated price monitoring solution that tracks product prices from Google Shopping daily and sends intelligent email notifications. Perfect for e-commerce monitoring, competitor analysis, deal hunting, and inventory management.

  ✨ Key Features
  🕘 Scheduled Monitoring: Daily automated price checks at 9 AM
  🛍️ Google Shopping Integration: Uses Bright Data's dataset for accurate pricing
  📊 Smart Price Comparison: Compares current prices with historical data
  📧 Intelligent Alerts: Sends emails only when prices meet criteria
  📈 Data Storage: Updates Google Sheets with latest pricing data
  🔄 Batch Processing: Handles multiple products with rate limiting
  ⚡ Fast & Reliable: Built-in error handling
  🎯 Customizable Filters: Advanced price comparison logic

  🎯 What This Workflow Does
  Schedule Trigger: Runs daily at 9 AM
  Data Retrieval: Fetches product list from Google Sheets
  Price Extraction: Scrapes current prices using Bright Data
  Data Update: Updates Google Sheets with new prices
  Price Comparison: Compares new vs. old prices
  Smart Filtering: Filters products that meet alert criteria
  Email Notifications: Sends alerts for qualifying changes
  Rate Limiting: Adds delay between emails

  Output Data Points

  | Field         | Description                | Example                          |
  | :------------ | :------------------------- | :------------------------------- |
  | Product URL   | Original Google Shopping URL | https://shopping.google.com/product/... |
  | Product Name  | Product title              | iPhone 15 Pro Max 256GB          |
  | Ratings       | Product rating score       | 4.5                              |
  | Reviews       | Number of reviews          | 1,247                            |
  | Old Price     | Previous price             | $1,199.00                        |
  | New Price     | Current scraped price      | $1,199.00                        |
  | Timestamp     | When the check occurred    | 2025-05-30T09:00:00Z             |

  🚀 Setup Instructions

  Prerequisites
  n8n instance (self-hosted or cloud)
  Google account with Sheets access
  Bright Data account with Google Shopping dataset access
  Gmail account for notifications

  Steps
  Import the workflow JSON into n8n
  Configure Bright Data credentials and dataset access
  Set up Google Sheets with required columns
  Configure Gmail OAuth2 credentials
  Update sheet IDs and schedule settings
  Test with sample products and activate

  📖 Usage Guide

  Google Sheet Structure
  Your Google Sheet should have the following columns to ensure the workflow functions correctly:

  Product URL** (Text): The direct URL to the Google Shopping product page. This is the primary identifier for the product.
  Product Name** (Text): The name of the product. This will be automatically populated or updated by the workflow.
  Old Price** (Number/Currency): The price of the product from the previous check. This column is crucial for price comparison.
  New Price** (Number/Currency): The most recently scraped price of the product.
  Ratings** (Number): The star rating of the product.
  Reviews** (Number): The total number of reviews for the product.
  Timestamp** (Datetime): The date and time when the price check was performed.

  Adding Products
  Add Google Shopping URLs to your Google Sheet.
  The workflow will fetch product details and track prices.
  Historical price data builds over time.

  Understanding Price Alerts
  The default setting for this workflow is to send an email alert when the new price equals the old price. This might seem counterintuitive, but it's useful for specific scenarios, such as:

  Monitoring stable pricing:** If you are tracking a product and want to be notified when its price has remained consistent over time, indicating a potential stable buying opportunity or a benchmark.
  Verifying data consistency:** To confirm that the scraping process is working correctly and consistently retrieving the same price when no changes are expected.

  You can easily customize the alert logic to trigger on different conditions as described below.

  Customizing Alert Logic
  Price drops:** new_price &lt; old_price
  Significant drops:** new_price &lt; (old_price * 0.9) (e.g., price dropped by more than 10%)
  Price increases:** new_price &gt; old_price
  Any change:** new_price != old_price

  Reading the Results
  Real-time pricing data
  Historical tracking
  Product metadata
  Timestamps for each check

  🔧 Customization Options
  Add More Data:** Descriptions, availability, seller info, shipping, images
  Modify Email Templates:** Customize subject and body
  Multiple Recipients:** Duplicate email node and change recipients
  Webhook Integration:** Add real-time triggers or Slack alerts

  🚨 Troubleshooting
  Bright Data connection failed:** Check API credentials and dataset access
  No price data extracted:** Verify URLs and test with different products
  Google Sheets permission denied:** Re-authenticate and check sharing
  Emails not sending:** Re-auth Gmail OAuth and verify recipients
  Filter not working:** Check price formats and logic
  Workflow failed:** Check logs, retry logic, and network status

  📊 Use Cases & Examples
  E-commerce Monitoring:** Track competitor pricing and trends
  Deal Hunting:** Get alerts for price drops on wishlist items
  Inventory Management:** Monitor supplier pricing for procurement
  Market Research:** Analyze pricing trends and generate reports

  ⚙️ Advanced Configuration
  Batch Processing:** Increase batch size, add delays, use parallel processing
  Price History:** Store historical data, calculate averages, forecast trends
  Tool Integration:** CRM, Slack, databases, BI tools (Tableau, Power BI)

  📈 Performance & Limits
  Single URL:** 2–5 seconds
  Concurrent Requests:** 3–5 (depends on Bright Data plan)
  Data Accuracy:** 95%+
  Success Rate:** 90%+
  Daily Capacity:** 100–500 products
  Memory:** ~100MB per execution
  API Calls:** 1 Bright Data + 2 Google Sheets per product

  🤝 Support & Community
  n8n Forum:** &lt;https://community.n8n.io&gt;
  Documentation:** &lt;https://docs.n8n.io&gt;
  Bright Data Support:** Via your Bright Data dashboard
  GitHub Issues:** Report bugs and request features

  🎯 Ready to Use!
  Your workflow provides a solid foundation for automated price monitoring. Customize it to fit your specific needs and use cases for maximum effectiveness in tracking Google Shopping prices with intelligent email notifications.

  Please note that this template uses Community Nodes. Ensure you understand the risks before using community nodes.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","@brightdata/n8n-nodes-brightdata.brightData","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.wait"]

workflow_json_compressed:
  [encoded data, 4140 characters]

metadata_json:
  {"categories":["automation","integration","data processing","monitoring","e-commerce"],"complexity":"medium","use_cases":["E-commerce price monitoring","Competitor price tracking","Deal hunting / alerting","Inventory / procurement price checks","Market research and pricing trend analysis"],"estimated_setup_minutes":30,"required_services":["n8n (self-hosted)","Bright Data (Google Shopping dataset)","Google Sheets (Google account)","Gmail (OAuth2)"],"key_features":["Scheduled daily monitoring (9 AM)","Google Shopping scraping via Bright Data","Smart price comparison and customizable alert logic","Email notifications for qualifying price changes","Updates and stores results in Google Sheets (historical tracking)"],"target_audience":["E-commerce managers","Marketing teams / growth hackers","Data analysts and market researchers','Procurement / inventory managers','Developers / automation engineers"]}


name: Extract Google My Business Leads by Service or Location with Bright Data
author_name: Dvir Sharon
author_username: dvirsharon
categories: []
created_at: 2025-06-01T11:52:57.062Z
updated_at: 2025-06-01T11:52:57.062Z
url: https://n8n.io/workflows/4555
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  📍 Extract Google My Business Leads by Service & Location with Bright Data to Google Sheets
  This template requires a self-hosted n8n instance to run.

  A comprehensive n8n automation that extracts Google My Business listings by service type and geographic location using Bright Data's Google Maps dataset, with intelligent city expansion and automatic duplicate removal.

  👥 Who is this for?
  Lead generation professionals
  Sales teams
  Marketing agencies
  Business development representatives
  Entrepreneurs conducting outreach or market research

  ❓ What problem is this solving?
  Manual lead generation from Google Maps is time-consuming and inefficient. This workflow automates the process of finding businesses by service type and location, expanding searches across cities, removing duplicates, and organizing results in a structured format.

  ⚙️ What this workflow does

  Input Processing
  Accepts service type, state, and country via web form
  Uses Claude AI to generate city lists
  Auto-categorizes services
  Creates search queries per city

  Data Collection
  Uses Bright Data's Google Maps dataset
  Processes in batches with rate limits
  Monitors scraping with retry logic
  Formats and handles API responses

  Quality Control
  Removes duplicates by name and phone
  Maintains clean data in Google Sheets
  Ensures structured, usable datasets

  📄 Output Data Points

  | Field           | Description                 | Example                       |
  | :-------------- | :-------------------------- | :---------------------------- |
  | Business Name   | Company or business name    | TechFix Computer Repair       |
  | Category        | Business category type      | Electronics                   |
  | Country         | Country location            | US                            |
  | City            | Specific city searched      | Austin                        |
  | Phone Number    | Contact phone number        | +1 (555) 123-4567             |
  | Website URL     | Business website            | https://techfix.com           |
  | Google Maps URL | Direct Maps link            | https://maps.google.com/... |
  | Address         | Full business address       | 123 Main St, Austin, TX       |
  | Operating Hours | Business hours              | Mon-Fri 9AM-6PM               |
  | Google Rating   | Star rating                 | 4.5                           |
  | Total Reviews   | Number of reviews           | 127                           |
  | Reviews URL     | Link to reviews             | https://maps.google.com/reviews... |

  🚀 Setup Instructions

  Prerequisites
  n8n instance (self-hosted or cloud)
  Google account with Sheets access
  Bright Data account with Google Maps dataset access
  Anthropic API key for Claude AI

  Step-by-Step
  Import the workflow JSON into n8n
  Configure Bright Data credentials and dataset access
  Set up Google Sheets and OAuth2 credentials
  Configure Claude AI with your API key
  Replace all placeholder credential IDs and tokens. For improved security, use credentials instead of hardcoding the API token placeholder in the HTTP Request node.
  Test with sample data (e.g., "Coffee Shop" in California, US)
  Activate the workflow and use the form for submissions

  🛠 How to Customize

  Modify Geographic Scope
  Add countries to the form dropdown
  Customize Claude prompts for city generation
  Adjust search logic for international markets

  Enhance Data Collection
  Add more fields from Bright Data
  Include revenue, employee count, social profiles

  Improve Duplicate Detection
  Use fuzzy matching for similar names
  Include address-based checks

  Customize Output Format
  Transform data for CRM compatibility
  Export to CSV, database, or multiple destinations

  Implement Advanced Features
  Integrate email finder services
  Include lead scoring logic
  Discover social media profiles

  Batch Processing Optimization
  Adjust batch sizes per Bright Data limits
  Use parallel processing and retry logic

  Integration Options
  Connect to CRMs like HubSpot or Salesforce
  Trigger email automation
  Integrate with marketing platforms

nodes_used:
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workflow_json_compressed:
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metadata_json:
  {"categories":["lead generation","web scraping","data enrichment","automation","integration"],"complexity":"complex","use_cases":["Generate targeted leads by service and city","Sales prospecting and outreach list building","Market research and competitor discovery","Automated data collection and enrichment","Populate CRMs or Google Sheets with structured business data"],"estimated_setup_minutes":60,"required_services":["n8n (self-hosted instance)","Bright Data (Google Maps dataset access)","Google Sheets (Google account with Sheets & OAuth2)","Anthropic (Claude) API key"],"key_features":["Extract Google My Business listings by service type and location","Claude AI-driven city list generation and service auto-categorization","Batch processing with rate limits and retry logic","Duplicate removal by name and phone, clean data maintenance","Outputs structured results to Google Sheets (configurable fields)"],"target_audience":["Lead generation professionals","Sales teams / Business development reps","Marketing agencies and consultants"]}


name: Auto-Publish Latest News on X with AI Content Generation using Keywords and Bright Data
author_name: Dvir Sharon
author_username: dvirsharon
categories: []
created_at: 2025-06-01T11:55:19.435Z
updated_at: 2025-06-01T11:55:19.435Z
url: https://n8n.io/workflows/4556
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  📰 Publish Latest News on X and Other Social Media Platforms Using Keyword

  A comprehensive n8n automation that fetches the latest news based on keywords, generates AI-powered social media content, and automatically publishes to X (Twitter) with complete tracking and notification systems.

  📋 Overview

  This workflow provides a professional news publishing solution that automatically discovers breaking news, creates engaging social media content using AI, and publishes to X (Twitter) with comprehensive tracking. Perfect for news organizations, content creators, social media managers, and businesses wanting to stay current with automated news sharing. The system uses BrightData's Google News dataset, OpenAI's GPT-4o for content generation, and multi-platform integration for complete automation.

  ⭐ Key Features

  📝 Form-Based Input**: Clean web form for keyword and country submission
  📰 Real-Time News Fetching**: BrightData Google News integration for latest articles
  🤖 AI Content Generation**: GPT-4o powered tweet creation with hashtags
  📱 Auto X Publishing**: Direct posting to X (Twitter) with URL tracking
  📊 Complete Tracking**: Google Sheets logging of all published content
  🔔 Email Notifications**: Success alerts with tweet links
  🌍 Multi-Country Support**: Localized news for US, India, UK, Australia
  ⚡ Status Monitoring**: Real-time progress tracking with retry logic
  🛡 Error Handling**: Robust error management and validation
  🔄 Loop Management**: Intelligent waiting for news processing completion

  🎯 What This Workflow Does

  Input:

  News Name**: Keyword or topic for news search (required)
  Country**: Target country for localized news (dropdown: US/IN/GB/AU)

  Processing:

  Form Submission: Captures news keyword and target country
  News Triggering: Initiates BrightData Google News scraping job
  Status Monitoring: Checks scraping progress with intelligent retry loop
  Data Retrieval: Fetches latest news articles when ready
  AI Content Creation: Generates engaging tweet content using GPT-4o
  Social Publishing: Posts content to X (Twitter) automatically
  URL Generation: Creates direct tweet links for tracking
  Data Logging: Saves content and URLs to Google Sheets
  Email Notification: Sends success confirmation with tweet link
  Completion: Workflow ends with full audit trail

  📋 Output Data Points

  | Field         | Description                         | Example                                                                                                |
  | :------------ | :---------------------------------- | :----------------------------------------------------------------------------------------------------- |
  | TweetMessage  | AI-generated social media content | "Breaking: AI revolution transforming healthcare with 40% efficiency gains. New study shows promising results in patient care automation. #AI #Healthcare #Innovation #TechNews #US" |
  | TweetURL      | Direct link to published tweet      | https://twitter.com/i/web/status/1234567890123456789                                                 |

  🛠️ Setup Instructions

  Prerequisites:

  n8n instance (self-hosted or cloud)
  X (Twitter) account with API v2 access
  OpenAI account with GPT-4o access
  Gmail account for notifications
  Google account with Sheets access
  BrightData account with Google News dataset access
  Basic understanding of social media automation

  Step 1: Import the Workflow

  Copy the JSON workflow code from the provided file.
  In n8n, click "+ Add workflow".
  Select "Import from JSON".
  Paste the workflow code and click "Import".
  The workflow will appear with all nodes properly connected.

  Step 2: Configure API Credentials

  X (Twitter) API Setup:

  Create X Developer Account at developer.twitter.com.
  Create new app and generate API keys.
  In n8n: Credentials → + Add credential → Twitter OAuth2 API.
  Add your Twitter API credentials:
      API Key
      API Secret Key
      Bearer Token
      Access Token
      Access Token Secret
  Test the connection with a sample tweet.

  OpenAI API Configuration:

  Get API key from platform.openai.com.
  Ensure GPT-4o model access is available.
  In n8n: Credentials → + Add credential → OpenAI API.
  Add your OpenAI API key.
  Verify model access in the "OpenAI Chat Model" node.

  Gmail Integration:

  Create "Gmail OAuth2" credential.
  Follow OAuth setup process.
  Grant email sending permissions.
  Test with sample email.

  BrightData News API:

  The workflow uses pre-configured token: 5662edde-6735-4c5d-a6c6-693043a5a9a5.
  Dataset ID: gd_lnsxoxzi1omrwnka5r (Google News).
  Verify access to Google News dataset.
  Test API connection.

  Google Sheets Integration:

  Create "Google Sheets OAuth2 API" credential.
  Complete OAuth authentication.
  Grant read/write permissions.
  Test connection.

  Step 3: Configure Google Sheets Integration

  Create Google Sheets Structure:
      Sheet Name: "Publish Latest News on Social Media Platforms Using Keyword"
      Tab: "Data" (default)
      Columns:
          Tweet Message: AI-generated content posted to X
          Tweet URL: Direct link to published tweet
  Sheet Configuration:
      Create new Google Sheet or use existing one.
      Add the required column headers.
      Copy Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit.
      Current configured Sheet ID: 1koxNrwdeuaSBdREuKc7JQh3d9blEk0sQDJ8VgVLjPOo.
  Update Workflow Settings:
      Open "Google Sheets" node.
      Replace Document ID with your Sheet ID.
      Select your Google Sheets credential.
      Choose "Data" sheet/tab.
      Verify column mapping is correct.

  Step 4: Configure Form Interface

  Form Settings:

  Open "On form submission" node.
  Form configuration:
      Title: "News Publisher"
      Description: "publish latest news to direct social media"
      Fields:
          News Name (text, required)
          Country (dropdown: US, IN, GB, AU, required)
      Webhook URL: Copy webhook URL from form trigger node.
      Current webhook ID: 8d320705-688c-4150-a393-cf899d2bbb52.
      Test form accessibility and submission.

  Step 5: Configure Email Notifications

  Gmail Setup:

  Open "Gmail" node.
  Update recipient email: raushan@iwantonlinemarketing.com.
  Email template includes:
      Success confirmation
      Direct tweet link
      Professional formatting
  Test email delivery.

  Step 6: Test the Workflow

  Sample Test Data:

  Use these examples for testing:

  | News Name             | Country | Expected Results                                   |
  | :-------------------- | :------ | :------------------------------------------------- |
  | artificial intelligence | US      | Latest AI news with US-specific hashtags           |
  | cricket world cup     | IN      | Sports news with India-focused content            |
  | brexit update         | GB      | UK political news with British hashtags            |
  | bushfire news         | AU      | Australian environmental news                      |

  Testing Process:

  Activate the workflow (toggle switch).
  Navigate to the webhook form URL.
  Submit test data.
  Monitor execution progress:
      News fetching (30-60 seconds)
      AI content generation (10-15 seconds)
      X publishing (5-10 seconds)
      Sheet update and email (5 seconds)
  Verify results in all platforms.

  📖 Usage Guide

  Using the Form Interface

  Navigate to the webhook URL provided by the form trigger.
  Enter news keyword or topic (e.g., "climate change", "stock market", "technology").
  Select target country from dropdown.
  Click submit and wait for processing.
  Check email for success notification with tweet link.

  Example Inputs to Test

  | News Name                         | Country | Expected                                               |
  | :-------------------------------- | :------ | :----------------------------------------------------- |
  | "artificial intelligence breakthrough" | "US"    | Latest AI developments with tech hashtags              |
  | "football premier league"         | "GB"    | UK football news with sports hashtags                  |
  | "stock market updates"            | "IN"    | Indian market news with finance hashtags                |
  | "hollywood movies"                | "AU"    | Entertainment news with Australian perspective         |

  Country-Specific Considerations

  United States (US)**:
      Focus on national news and global impact.
      Hashtags: #USA, #American, #Breaking, #News.
      Time zone considerations for optimal posting.
  India (IN)**:
      Emphasis on regional relevance.
      Hashtags: #India, #Indian, #News, #Breaking.
      Cultural context in content generation.
  United Kingdom (GB)**:
      British perspective and terminology.
      Hashtags: #UK, #British, #News, #Breaking.
      Focus on European context.
  Australia (AU)**:
      Australian angle and regional focus.
      Hashtags: #Australia, #Australian, #News, #Breaking.
      Pacific region context.

  📊 Reading the Results

  Google Sheets Data

  The output sheet contains:

  Complete tweet content with hashtags and formatting.
  Direct tweet URLs for easy access and sharing.
  Chronological record of all published content.
  Audit trail for content management.

  Email Notifications

  Success emails include:

  Confirmation that content was published.
  Direct link to view the tweet.
  Professional formatting for easy reference.

  X (Twitter) Posts

  Published content features:

  AI-optimized messaging within 260 character limit.
  Relevant hashtags based on topic and country.
  Engaging format designed for social media.
  Professional tone suitable for news sharing.

  🔧 Customization Options

  Expanding Social Media Platforms

  Add more platforms to the publishing workflow:

  // Add LinkedIn publishing
  {
    "node": "LinkedIn",
    "type": "n8n-nodes-base.linkedin",
    "parameters": {
      "text": "={{ $json.output }}",
      "additionalFields": {}
    }
  }
  // Add Facebook posting
  {
    "node": "Facebook",
    "type": "n8n-nodes-base.facebook",
    "parameters": {
      "pageId": "YOUR_PAGE_ID",
      "message": "={{ $json.output }}"
    }
  }

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metadata_json:
  {"categories":["automation","integration","social media","news aggregation","content generation"],"complexity":"complex","use_cases":["Automated publishing of breaking news to X (Twitter)","AI-generated social media copy from news articles","Localized/multi-country news distribution","Content tracking and audit logging","Email notifications and workflow monitoring"],"estimated_setup_minutes":60,"required_services":["n8n (self-hosted or cloud)","BrightData (Google News dataset)","OpenAI (GPT-4o)","X / Twitter API v2","Google Sheets (Google account)","Gmail (for notifications)"],"key_features":["Form-based keyword and country input (webhook)","BrightData Google News scraping and status monitoring","GPT-4o AI content generation with hashtags","Automatic posting to X (Twitter) with URL generation","Google Sheets logging of published items (audit trail + tracking links)"],"target_audience":["news organizations","social media managers","content creators and marketers"]}


name: Intelligent Email Organization with AI-Powered Content Classification for Gmail
author_name: Niranjan G
author_username: niranjan
categories: []
created_at: 2025-06-01T12:02:07.509Z
updated_at: 2025-06-01T12:02:07.509Z
url: https://n8n.io/workflows/4557
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow leverages AI to intelligently analyze incoming Gmail messages and automatically apply relevant labels based on the email content.

  The default configuration includes the following labels:

  Newsletter**: Subscription updates or promotional content.
  Inquiry**: Emails requesting information or responses.
  Invoice**: Billing and payment-related emails.
  Proposal**: Business offers or collaboration opportunities.
  Action Required**: Emails demanding immediate tasks or actions.
  Follow-up Reminder**: Emails prompting follow-up actions.
  Task**: Emails containing actionable tasks.
  Personal**: Non-work-related emails.
  Urgent**: Time-sensitive or critical communications.
  Bank**: Banking alerts and financial statements.
  Job Update**: Recruitment or job-related communications.
  Spam/Junk**: Unwanted or irrelevant bulk emails.
  Social/Networking**: Notifications from social platforms.
  Receipt**: Purchase confirmations and receipts.
  Event Invite**: Invitations or calendar-related messages.
  Subscription Renewal**: Reminders for subscription expirations.
  System Notification**: Technical alerts from services or systems.

  You can customize labels and definitions based on your specific use case.

  How it works:

  The workflow periodically retrieves new Gmail messages.
  Only emails without existing labels, regardless of read status, are sent to the AI for analysis.
  Email content (subject and body) is analyzed by an AI model to determine the appropriate label.
  Labels identified by the AI are applied to each email accordingly.

  Note: This workflow performs 100% better than the default Gmail trigger method, which is why the workflow was switched from Gmail trigger to a scheduled workflow. By selectively processing only unlabeled emails, it ensures comprehensive labeling while significantly reducing AI processing costs.

  Setup Steps:

  Configure credentials for Gmail and your chosen AI service (e.g., OpenAI).
  Ensure labels exist in your Gmail account matching the workflow definitions.
  Adjust the AI prompt to match your labeling needs.
  Optionally customize the polling interval (default: every 2 minutes).

  This workflow streamlines your email management, keeping your inbox organized effortlessly while optimizing resource usage.

nodes_used:
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metadata_json:
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name: Generate Complete Business Plans with Customizable AI Models and Specialized Agents
author_name: Sina
author_username: sina2266
categories: []
created_at: 2025-06-01T12:06:15.823Z
updated_at: 2025-06-01T12:06:15.823Z
url: https://n8n.io/workflows/4558
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  👔 Who is this for?
  Entrepreneurs and startup founders preparing for investors  
  Business consultants drafting complete client plans  
  Strategy teams building long-term business models  
  Accelerators, incubators, or pitch trainers  

  ❓ What problem does this workflow solve?
  Writing a full business plan takes days of work, multiple tools, and often gets stuck in messy docs or slides. This template automates every major section, generating a clean, detailed, and professional business plan with AI in just minutes.

  ⚙️ What this workflow does
  Starts with a chat message asking for your business idea or startup concept  
  Passes the idea through 83 intelligent agents, each handling a full business plan chapter:
    Executive Summary  
    Problem & Solution  
    Product Description  
    Market Research  
    Competitor Analysis  
    Business Model  
    Marketing Strategy (includes guerrilla ideas)  
    Operational Plan  
    Financial Plan  
    Team & Advisors  
    Roadmap  
    Conclusion & Next Steps  
  Each section uses tailored prompts and business logic  
  Combines all outputs into a structured, professional Markdown file  
  Final result: a ready-to-export business plan in seconds  

  🛠️ Setup
  Import this template into your n8n instance  
  Replace the “LLM Chat Model” node with your preferred model (Ollama, GPT-4, etc.)  
  Start from the chat input node — describe your startup or idea  
  Wait for all agents to finish  
  Download the final Business plan file 

  🤖 LLM Flexibility (Choose Your Model)
  Supports:
    OpenAI (GPT-4 / GPT-3.5)
    Ollama (LLaMA 3.1, Mistral, DeepSeek, etc.)
    Any compatible N8N chat model  
  To change the model, just replace the “Language Model” node — no other updates required  


  📌 Notes
  All nodes are clearly named by function (e.g., “Market Research Generator”)  
  Sticky notes included for clarity  
  Generates high-quality plans suitable for VCs or accelerators  
  Modular: you can turn off or reorder any chapter  

  📩 Need help?
  Email: sinamirshafiee@gmail.com  
  Happy to support setup, LLM switching, or custom section development.

nodes_used:
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  [encoded data, 40200 characters]

metadata_json:
  {"categories":["automation","document-generation","strategy","startup-tools","content-generation"],"complexity":"complex","use_cases":["Rapidly generate investor-ready business plans","Consultants drafting detailed client plans","Strategy teams building long-term business models","Accelerator/incubator pitch prep and training","Creating structured market & competitor analysis documents"],"estimated_setup_minutes":20,"required_services":["n8n instance","Large language model provider (OpenAI GPT-4/GPT-3.5, Ollama, LLaMA variants, or any compatible n8n chat model)","Optional: file export/storage destination (S3, Google Drive, local filesystem)"],"key_features":["Automates every major business-plan section via 83 specialized agents","Tailored prompts and business logic per chapter (Executive Summary, Market Research, Financial Plan, etc.)","Merges section outputs into a single, structured Markdown business plan","Modular design — enable/disable or reorder chapters","Easy LLM swap: replace the Language Model node to change providers/models without other edits"],"target_audience":["Entrepreneurs and startup founders","Business consultants and advisors","Strategy and product teams at startups/SMBs"]}


name: Intelligent Web & Local Search with Brave Search API and Google Gemini MCP Server
author_name: Jez
author_username: jez
categories: []
created_at: 2025-06-01T12:10:43.912Z
updated_at: 2025-06-01T12:10:43.912Z
url: https://n8n.io/workflows/4559
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Summary
  This n8n workflow implements an AI-powered agent that intelligently uses the Brave Search API (via an external MCP service like Smithery) to perform both web and local searches. It understands natural language queries, selects the appropriate search tool, and exposes this enhanced capability as a single, callable MCP tool.

  Key Features
  🤖 Intelligent Tool Selection: AI agent decides between Brave's web search and local search tools based on user query context.
  🌐 MCP Microservice: Exposes complex search logic as a single, easy-to-integrate MCP tool (call_brave_search_agent).
  🧠 Powered by Google Gemini: Utilizes the gemini-2.5-flash-preview-05-20 LLM for advanced reasoning.
  🗣️ Conversational Memory: Remembers context within a single execution flow.
  📝 Customizable System Prompt: Tailor the AI's behavior and responses.
  🧩 Modular Design: Connects to external Brave Search MCP tools (e.g., from Smithery).

  Benefits
  🔌 Simplified Integration: Easily add advanced, AI-driven search capabilities to other applications or agent systems.
  💸 Reduced Client-Side LLM Costs: Offloads complex prompting and tool orchestration to n8n, minimizing token usage for client-side LLMs.
  🔧 Centralized Logic: Manage and update search strategies and AI behavior in one place.
  🚀 Extensible: Can be adapted to use other search tools or incorporate more complex decision-making.

  Nodes Used
  @n8n/n8n-nodes-langchain.mcpTrigger (MCP Server Trigger)
  @n8n/n8n-nodes-langchain.toolWorkflow
  @n8n/n8n-nodes-langchain.agent (AI Agent)
  @n8n/n8n-nodes-langchain.lmChatGoogleGemini (Google Gemini Chat Model)
  n8n-nodes-mcp.mcpClientTool (MCP Client Tool - for Brave Search)
  @n8n/n8n-nodes-langchain.memoryBufferWindow (Simple Memory)
  n8n-nodes-base.executeWorkflowTrigger (Workflow Start - for direct execution/testing)

  Prerequisites
  An active n8n instance (v1.22.5+ recommended).
  A Google AI API key for using the Gemini LLM.
  Access to an external MCP service that provides Brave Search tools (e.g., a Smithery account configured with their Brave Search MCP). This includes the MCP endpoint URL and any necessary authentication (like an API key for Smithery).

  Setup Instructions
  Import Workflow: Download the Brave_Search_Smithery_AI_Agent_MCP_Server.json file and import it into your n8n instance.
  Configure LLM Credential:
      Locate the 'Google Gemini Chat Model' node.
      Select or create an n8n credential for "Google Palm API" (used for Gemini), providing your Google AI API key.
  Configure Brave Search MCP Credential:
      Locate the 'brave_web_search' and 'brave_local_search' (MCP Client) nodes.
      Create a new n8n credential of type "MCP Client HTTP API".
          Name: e.g., Smithery Brave Search Access
          Base URL: Enter the URL of your Brave Search MCP endpoint from your provider (e.g., https://server.smithery.ai/@YOUR_PROFILE/brave-search/mcp).
          Authentication: If your MCP provider requires an API key, select "Header Auth". Add a header with the name (e.g., X-API-Key) and value provided by your MCP service.
      Assign this newly created credential to both the 'brave_web_search' and 'brave_local_search' nodes.
  Note MCP Trigger Path:
      Open the 'Brave Search MCP Server Trigger' node.
      Copy its unique 'Path' (e.g., /cc8cc827-3e72-4029-8a9d-76519d1c136d). You will combine this with your n8n instance's base URL to get the full endpoint URL for clients.

  How to Use
  This workflow exposes an MCP tool named call_brave_search_agent. External clients can call this tool via the URL derived from the 'Brave Search MCP Server Trigger'.

  Example Client MCP Configuration (e.g., for Roo Code):
  "n8n-brave-search-agent": {
    "url": "https://YOUR_N8N_INSTANCE/mcp/cc8cc827-3e72-4029-8a9d-76519d1c136d/sse",
    "alwaysAllow": [
      "call_brave_search_agent"
    ]
  }
  Replace YOUR_N8N_INSTANCE with your n8n's public URL and ensure the path matches your trigger node.

  Example Request:
  Send a POST request to the trigger URL with a JSON body:
  {
    "input": { "query": "best coffee shops in London" }
  }



  The agent will stream its response, including the summarized search results.

  Customization
  AI Behavior:* Modify the System Prompt within the *'Brave Search AI Agent'** node to fine-tune its decision-making, response style, or how it uses the search tools.
  LLM Choice:* Replace the *'Google Gemini Chat Model'** node with any other compatible LLM node supported by n8n.
  Search Tools:** Adapt the workflow to use different or additional search tools by modifying the MCP Client nodes and updating the AI Agent's system prompt and tool definitions.

  Further Information
  GitHub Repository: https://github.com/jezweb/n8n
  The workflow includes extensive sticky notes for in-canvas documentation.

  Author
  Jeremy Dawes (Jezweb)

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-mcp.mcpClientTool","n8n-nodes-mcp.mcpClientTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 9732 characters]

metadata_json:
  {"categories":["automation","integration","AI/agent","search"],"complexity":"medium","use_cases":["AI-powered web and local search","Exposing search logic as an MCP microservice/tool","Integrating advanced search into apps or agent systems","Conversational search with context-aware results","Centralizing search orchestration and prompt logic"],"estimated_setup_minutes":30,"required_services":["n8n instance (v1.22.5+)","Google AI (Gemini) API key","External MCP service providing Brave Search tools (e.g., Smithery) and its endpoint","Publicly accessible URL for n8n (for MCP trigger access)"],"key_features":["AI-driven selection between web and local Brave Search tools","Exposes a single MCP tool (call_brave_search_agent) for clients","Uses Google Gemini LLM for reasoning and summarization","Conversational memory within an execution flow","Customizable system prompt and modular node design (MCP clients, agent, memory)"],"target_audience":["developers integrating search into apps","AI/ML engineers building agent workflows","DevOps/Platform engineers managing n8n microservices"]}


name: Build an AI IT Support Agent with Azure Search, Entra ID & Jira
author_name: Adam Bertram
author_username: adbertram
categories: []
created_at: 2025-06-01T12:27:26.929Z
updated_at: 2025-06-01T12:27:26.929Z
url: https://n8n.io/workflows/4560
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  An intelligent IT support agent that uses Azure AI Search for knowledge retrieval, Microsoft Entra ID integration for user management, and Jira for ticket creation. The agent can answer questions using internal documentation and perform administrative tasks like password resets.

  How It Works

  The workflow operates in three main sections:

  Agent Chat Interface: A chat trigger receives user messages and routes them to an AI agent powered by Google Gemini. The agent maintains conversation context using buffer memory and has access to multiple tools for different tasks.

  Knowledge Management: Users can upload documentation files (.txt, .md) through a form trigger. These documents are processed, converted to embeddings using OpenAI's API, and stored in an Azure AI Search index with vector search capabilities.

  Administrative Tools: The agent can query Microsoft Entra ID to find users, reset passwords, and create Jira tickets when issues need escalation. It uses semantic search to find relevant internal documentation before responding to user queries.

  The workflow includes a separate setup section that creates the Azure AI Search service and index with proper vector search configuration, semantic search capabilities, and the required field schema.

  Prerequisites

  To use this template, you'll need:

  n8n cloud or self-hosted instance
  Azure subscription with permissions to create AI Search services
  Microsoft Entra ID (Azure AD) access with user management permissions
  OpenAI API account for embeddings
  Google Gemini API access
  Jira Software Cloud instance
  Basic understanding of Azure resource management

  Setup Instructions

  Import the template into n8n.

  Configure credentials:
     Add Google Gemini API credentials
     Add OpenAI API credentials for embeddings
     Add Microsoft Azure OAuth2 credentials with appropriate permissions
     Add Microsoft Entra ID OAuth2 credentials
     Add Jira Software Cloud API credentials

  Update workflow parameters:
     Open the "Set Common Fields" nodes
     Replace &lt;azure subscription id&gt; with your Azure subscription ID
     Replace &lt;azure resource group&gt; with your target resource group name
     Replace &lt;azure region&gt; with your preferred Azure region
     Replace &lt;azure ai search service name&gt; with your desired service name
     Replace &lt;azure ai search index name&gt; with your desired index name
     Update the Jira project ID in the "Create Jira Ticket" node

  Set up Azure infrastructure:
     Run the manual trigger "When clicking 'Test workflow'" to create the Azure AI Search service and index
     This creates the vector search index with semantic search configuration

  Configure the vector store webhook:
     Update the "Invoke Query Vector Store Webhook" node URL with your actual webhook endpoint
     The webhook URL should point to the "Semantic Search" webhook in the same workflow

  Upload knowledge base:
     Use the "On Knowledge Upload" form to upload your internal documentation
     Supported formats: .txt and .md files
     Documents will be automatically embedded and indexed

  Test the setup:
     Use the chat interface to verify the agent responds appropriately
     Test knowledge retrieval with questions about uploaded documentation
     Verify Entra ID integration and Jira ticket creation

  Security Considerations

  Use least-privilege access for all API credentials
  Microsoft Entra ID credentials should have limited user management permissions
  Azure credentials need Search Service Contributor and Search Index Data Contributor roles
  OpenAI API key should have usage limits configured
  Jira credentials should be restricted to specific projects
  Consider implementing rate limiting on the chat interface
  Review password reset policies and ensure force password change is enabled
  Validate all user inputs before processing administrative requests

  Extending the Template

  You could enhance this template by:

  Adding support for additional file formats (PDF, DOCX) in the knowledge upload
  Implementing role-based access control for different administrative functions
  Adding integration with other ITSM tools beyond Jira
  Creating automated escalation rules based on query complexity
  Adding analytics and reporting for support interactions
  Implementing multi-language support for international organizations
  Adding approval workflows for sensitive administrative actions
  Integrating with Microsoft Teams or Slack for notifications

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.jiraTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.formTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

workflow_json_compressed:
  [encoded data, 10832 characters]

metadata_json:
  {"categories":["automation","integration","knowledge_management","ai_assistant","it_support"],"complexity":"complex","use_cases":["Internal IT helpdesk chat assistant","Semantic search over internal documentation","Automated user management (e.g., password reset) via Entra ID","Automatic Jira ticket creation and escalation","Ingesting and indexing knowledge files for vector search"],"estimated_setup_minutes":90,"required_services":["n8n (cloud or self-hosted)","Azure subscription (Azure AI Search / Cognitive Search)","Microsoft Entra ID (Azure AD) with user management permissions","OpenAI API (embeddings)","Google Gemini API (agent LLM)","Jira Software Cloud"],"key_features":["Chat-based AI agent with conversation memory","Semantic/vector search using Azure AI Search","Document ingestion pipeline (txt, md → embeddings → index)","Administrative tools: user lookup and password reset via Entra ID","Automated Jira ticket creation for escalations"],"target_audience":["IT administrators","ITSM teams","Platform/DevOps engineers implementing internal tooling integrations"]}


name: 🏤 Scrapping of European Union Events with Google Sheets
author_name: Samir Saci
author_username: samirsaci
categories: []
created_at: 2025-06-01T14:47:44.144Z
updated_at: 2025-06-01T14:47:44.144Z
url: https://n8n.io/workflows/4561
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Tags: Scrapping, Events, European Union, Networking

  Context

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

  We use AI, automation, and data to support sustainable and data-driven operations across all types of organizations.

  This workflow is part of our networking strategy (as a business) to track official EU events that may relate to topics we cover.



  &gt; Want to stay ahead of critical EU meetings and events without checking the website every day?

  This n8n workflow automatically scrapes the EU’s official event portal and logs the latest entries with clean metadata including date, location, category, and link.

  📬 For collaborations, feel free to connect with me on LinkedIn

  Who is this template for?

  This workflow is useful for:
  Policy & public affairs teams** following institutional activities  
  Sustainability teams** watching for relevant climate-related summits  
  NGOs and researchers** interested in event calendars  
  Data teams** building dashboards on public event trends

  What does it do?

  This n8n workflow:
  🌐 Scrapes the EU events portal for new meetings and conferences  
  📅 Extracts event metadata (title, date, location, type, and link)  
  🔁 Handles pagination across multiple pages  
  🚫 Checks for duplicates already stored  
  📊 Saves new records into a connected Google Sheet

  How it works



  Triggered daily via cron
  HTTP node loads the event listing HTML
  Extract HTML blocks for each event article
  Parse event name, link, type, location, and full date
  Concatenate and clean dates for easy tracking
  Store non-duplicate entries in Google Sheets

  The workflow uses static data to track pagination and ensure only new events are stored, making it ideal for building up a clean dataset over time.

  What do I need to get started?

  You’ll need:
  A Google Sheet connected to your n8n instance  
  No code or AI tools needed — just n8n and this template

  Follow the Guide!

  Sticky notes are included directly inside the workflow to guide you step-by-step through setup and customisation.


  🎥 Watch My Tutorial

  Notes

  This is ideal for analysts and consultants who want clean, structured data from the EU portal  
  You can add filtering, email alerts, or AI classifiers later

  This workflow was built using n8n version  1.93.0  
  Submitted: June 1, 2025

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.html","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.html","n8n-nodes-base.merge","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5008 characters]

metadata_json:
  {"categories":["automation","integration","data collection","web scraping","monitoring"],"complexity":"medium","use_cases":["Track and log official EU meetings and conferences","Build a time-series dataset of public events for analysis or dashboards","Alerting or filtering for topic-specific EU events (extendable)","Research and monitoring for policy, sustainability, or NGO work","Populate Google Sheets for downstream reporting or pipelines"],"estimated_setup_minutes":20,"required_services":["n8n instance with internet access","Google Sheets (Google account & connected credential)","EU Events public portal (HTTP access to event listings)"],"key_features":["Daily scheduled trigger (cron)","HTTP scraping of the EU events portal","HTML extraction and parsing of event blocks (title, date, location, type, link)","Pagination handling across listing pages","Duplicate detection to only store new entries in Google Sheets」「Data cleaning/concatenation of dates for consistent formatting"],"target_audience":["Policy & public affairs teams","Sustainability teams and consultants","NGOs and researchers tracking institutional events and meetings of interest to their topics of study or advocacy teams]"]}


name: Monitor Elderly Health Vitals & Send Alerts with Apple Health, Twilio & Gmail
author_name: Praveena
author_username: pankstr
categories: []
created_at: 2025-06-01T17:03:59.861Z
updated_at: 2025-06-01T17:03:59.861Z
url: https://n8n.io/workflows/4563
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  What is Elderwatch
  Elder Watch is a simple system that checks daily vitals — like heart rate, oxygen, and walking symmetry — using data from an iPhone or Apple Watch.
  If something looks off — say oxygen drops or heart rate spikes — it flags that as “attention required.”
  And depending on that status, it can either:
  Email a daily report to a caregiver
  Or if there’s an alert — trigger a phone call via Twilio
  Why do we need this
  Elder Watch can help older people living alone for children or care givers to keey an eye on without obsessively checking apps.
  It’s useful for clinics that run home-care programs.

  Requirements
  Self hosted or cloud N8N
  Apple health vis iphone/watch
  Twilio VOIP phone number (to place a call)

  Workflows
  Core workflow for getting health data, processing and making a phone call.
  Twilio workflow to invoke Calls API to place an outbound voice call. 

  twilio workflow
  {
    "name": "Twilio Bridge Caller copy",
    "nodes": [
      {
        "parameters": {
          "httpMethod": "POST",
          "path": "twilio-call",
          "responseMode": "responseNode",
          "options": {}
        },
        "type": "n8n-nodes-base.webhook",
        "typeVersion": 2,
        "position": [
          0,
          0
        ],
        "id": "ca3e6c69-3e7f-4d28-b699-4789a6fa2a6d",
        "name": "Webhook",
        "webhookId": "eb3d63df-800c-401d-931a-c6fba7d834ae"
      },
      {
        "parameters": {
          "respondWith": "text",
          "responseBody": "={{ $json.body }}",
          "options": {
            "responseCode": 200,
            "responseHeaders": {
              "entries": [
                {
                  "name": "Content-Type",
                  "value": "text/xml"
                }
              ]
            }
          }
        },
        "type": "n8n-nodes-base.respondToWebhook",
        "typeVersion": 1.1,
        "position": [
          580,
          0
        ],
        "id": "6587b7e2-ace8-4e2b-9f4b-ed028a363c25",
        "name": "Respond to Webhook"
      },
      {
        "parameters": {
          "jsCode": "const summary = $input.first().json.query.summary || 'No summary, check mail for critical health info';\n\nreturn [\n  {\n    json: {\n      body: &lt;Response&gt;\n  &lt;Say voice=\"alice\"&gt;${summary}&lt;/Say&gt;\n&lt;/Response&gt;\n    }\n  }\n];\n"
        },
        "type": "n8n-nodes-base.code",
        "typeVersion": 2,
        "position": [
          340,
          0
        ],
        "id": "0d4abf87-daf3-4533-8811-64ae61265f5d",
        "name": "Voice Twilio response"
      }
    ],
    "pinData": {
      "Webhook": [
        {
          "json": {
            "headers": {
              "host": "n8n.domain.com",
              "user-agent": "curl/8.7.1",
              "content-length": "0",
              "accept": "/",
              "accept-encoding": "gzip, br"
              
            },
            "params": {},
            "query": {
              "lead": " 44711111111111"
            },
            "body": {},
            "webhookUrl": "https://n8n.domain.com/webhook/twilio-call",
            "executionMode": "production"
          }
        }
      ]
    },
    "connections": {
      "Webhook": {
        "main": [
          [
            {
              "node": "Voice Twilio response",
              "type": "main",
              "index": 0
            }
          ]
        ]
      },
      "Voice Twilio response": {
        "main": [
          [
            {
              "node": "Respond to Webhook",
              "type": "main",
              "index": 0
            }
          ]
        ]
      }
    },
    "active": true,
    "settings": {
      "executionOrder": "v1"
    },
    "versionId": "b58c5a12-75be-4b1d-b144-8c7251468021",
    "meta": {
      "instanceId": "8dc0e8a0878d0086b2f46ef04bb00ae07186c936d82d0f0a67563e9652996d33"
    },
    "id": "RHaKqf8Wqt7fIuGH",
    "tags": []
  }

  Samples

  Resources
  https://www.youtube.com/watch?v=HYk5_jtMlgc

  Questions/Support
  Contact me on info@pankstr.com. 

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

workflow_json_compressed:
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metadata_json:
  {"categories":["health monitoring","automation","integration","notifications","remote care"],"complexity":"medium","use_cases":["Remote monitoring of elderly vitals (heart rate, SpO2, walking symmetry)","Daily summary reports to caregivers via email","Immediate voice alerts for critical/abnormal readings","Home-care clinic remote patient checks","Automated triage to reduce manual app-checking by caregivers"],"estimated_setup_minutes":45,"required_services":["n8n (self-hosted or cloud) with a public HTTPS webhook endpoint","Apple Health (iPhone / Apple Watch) as data source (or a mechanism to push Apple Health data to n8n)","Twilio account with an outbound-capable phone number (Calls API)","Gmail account (or SMTP) for daily email reports","Optional: tunneling service (ngrok) or DNS & TLS for webhook accessibility"],"key_features":["Ingest daily vitals from Apple Health (iPhone/Watch)","Evaluate vitals and mark status as 'attention required' when thresholds exceeded","Send daily email summaries to caregivers","Trigger outbound voice calls via Twilio for alerts using a TwiML response","Webhook-based Twilio bridge with dynamic voice message content (code node builds TwiML response)"],"target_audience":["Family caregivers / adult children monitoring elderly relatives","Home-care and remote patient monitoring clinics","Healthcare IT staff implementing lightweight monitoring automations in n8n"]}


name: 🚚 Estimate Driving Time and Distance for Logistics with Open Route API
author_name: Samir Saci
author_username: samirsaci
categories: []
created_at: 2025-06-01T17:58:05.758Z
updated_at: 2025-06-01T17:58:05.758Z
url: https://n8n.io/workflows/4564
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-30 08:33:37
description:
  Tags*: Supply Chain, Logistics, Route Planning, Transportation, GPS API

  Context

  Hi! I’m Samir — a Supply Chain Engineer and Data Scientist based in Paris, and founder of LogiGreen Consulting.

  I help companies improve their logistics operations using data, AI, and automation to reduce costs and minimize environmental footprint.

  &gt; Let’s use n8n to build smarter and greener transport operations!

  📬 For business inquiries, you can add find me on LinkedIn

  Who is this template for?

  This workflow is designed for logistics and transport teams who want to automate distance and travel time calculations for truck shipments.



  Ideal for:
  Control tower dashboards  
  Transport cost simulations  
  Route optimization studies 

  How does it work?

  This n8n workflow connects to a Google Sheet where you store city-to-city shipment lanes, and uses the OpenRouteService API to calculate:

  📏 Distance (in meters)  
  ⏱️ Travel time (in seconds)  
  🪪 Number of route steps  

  Steps:
  ✅ Load departure/destination city coordinates from a Google Sheet  
  🔁 Loop through each record  
  🚚 Query OpenRouteService using the truck (driving-hgv) profile  
  🧾 Extract and store results: distance, duration, number of steps  
  📤 Update the Google Sheet with new values  

  What do I need to get started?

  This workflow is beginner-friendly and requires:

  A Google Sheet with route pairs (departure and destination coordinates)
  A free OpenRouteService API key  
    👉 Get one here

  Next Steps

  🗒️ Follow the sticky notes inside the workflow to:

  Select your sheet
  Plug in your API key
  Launch the flow!


  🎥 Check the Tutorial

  🚀 You can customize the workflow to:
  Add CO2 emission estimates for Sustainability Reporting
  Connect to your TMS via API or EDI

  This template was built using n8n v1.93.0  
  Submitted: June 1, 2025

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

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metadata_json:
  {"categories":["logistics","route planning","automation","data processing"],"complexity":"medium","use_cases":["Estimate driving time and distance for shipments","Bulk route/distance matrix generation from spreadsheets","ETA calculations for deliveries","Feeding route metrics into optimization or reporting pipelines","Generating greener route decisions / footprint estimates"],"estimated_setup_minutes":15,"required_services":["OpenRouteService (Open Route API)","Spreadsheet/data source (e.g., Google Sheets or CSV)","n8n instance (with HTTP/Webhook access)"],"key_features":["Call OpenRoute API to compute driving time and distance","Batch processing of multiple routes/rows (splitInBatches)","Read/write integration with spreadsheets","Manual or webhook triggers to start workflows","Optional AI/ML node to enrich or adjust estimates (e.g., ETA corrections)"],"target_audience":["Supply chain engineers","Logistics planners and transportation managers","Operations teams managing deliveries/EOAs (end-of-arrival) planning) — note: remove parentheses? Actually just 'operations teams' )"]}


name: Analyze Crunchbase Startups by Keyword with Bright Data, Gemini AI & Google Sheets
author_name: Gleb D
author_username: glebd
categories: []
created_at: 2025-06-01T19:35:53.627Z
updated_at: 2025-06-01T19:35:53.627Z
url: https://n8n.io/workflows/4565
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow automates the discovery, enrichment, and comparative analysis of startups from the Crunchbase dataset via Bright Data, enhanced with AI, and exports structured results to Google Sheets.

  🚀 What It Does
  Receives a keyword from the user that describes the area of interest — such as an industry, sector, technology, or trend (e.g., "AI in healthcare", "carbon capture", "edtech"). 
  This keyword is used to filter relevant startups from the Crunchbase dataset via Bright Data.
  Fetches data from Bright Data's Crunchbase snapshot API.
  Extracts and cleans key fields from the JSON response.
  Sorts startups by most recent founding date.
  Selects the top 10 most recent companies.
  Sends these 10 companies to Google Gemini AI for comparative analysis.
  Embeds the AI-generated summary into the final export.

  Appends results to a Google Sheet for tracking and reporting.

  🛠️ Step-by-Step Setup
  Get user keyword input from a form.
  Use 3 Bright Data requests:
  Start snapshot.
  Poll snapshot status until ready.
  Fetch snapshot data in JSON format.
  Use a Python Code node to:
  Parse and sort companies by founded_date.
  Clean and standardize data fields.
  Pass the top 10 companies into Gemini AI for comparative insight.
  Merge the AI output back with company data.
  Send everything to Google Sheets.

  🧠 How It Works
  Snapshot Control: Polls every few seconds until the Bright Data snapshot is complete.
  Code Cleanup: Ensures consistent structure and formatting across all records.
  Comparative AI Analysis: Gemini compares all 10 companies at once and returns a unified analysis.
  Merging Output: AI analysis is merged into the first company’s record (to avoid duplication), while all 10 are exported.

  📤 Google Sheet Output
  Each row includes:
  name, 
  founded, 
  about, 
  num_employees, 
  type, 
  ipo_status, 
  full_description, 
  social_media_links, 
  address, 
  website, 
  funding_total, 
  num_investors, 
  lead_investors, 
  founders, 
  products_and_services, 
  monthly_visits, 
  crunchbase_link, 
  ai_analysis.

  AI comparative analysis summary (only once per batch – attached to the first company).
  All fields from above customizible through the python code (you can add additional ones from Bright Data output).

  🔐 Required Credentials
  Bright Data* – Replace *YOUR_API_KEY** in 3 HTTP Request nodes.
  Google Gemini API** – For AI analysis.
  Google Sheets OAuth2** – For spreadsheet export.

  ⚠️ Notes
  AI output is shared once per batch of 10 companies, attached to the first company entry. You can configure the limit of batch size in the first "Code" node.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.formTrigger","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
  [encoded data, 4608 characters]

metadata_json:
  {"categories":["integration","data_enrichment","ai_analysis","market_research"],"complexity":"medium","use_cases":["Startup discovery and tracking","Competitive analysis and comparative summaries","Investor/VC due diligence","Lead generation and partnership scouting","Market trend research and monitoring"],"estimated_setup_minutes":45,"required_services":["Bright Data (Crunchbase snapshot API)","Google Gemini AI (Gemini API)","Google Sheets (OAuth2)"],"key_features":["Keyword-driven Crunchbase snapshot queries via Bright Data","Polling snapshot status until data ready","JSON parsing, cleaning and standardization via Python code node","Sorting by founding date and selecting top N (default 10)","Batch comparative analysis using Google Gemini AI (single unified summary)"],"target_audience":["market researchers","investors and VCs","startup scouts and BD teams」「data engineers/automation builders"]}


name: Automated Book Summarization with DeepSeek AI, Qdrant Vector DB & Google Drive
author_name: Adam Crafts
author_username: adamcrafts
categories: []
created_at: 2025-06-01T19:36:11.215Z
updated_at: 2025-06-01T19:36:11.215Z
url: https://n8n.io/workflows/4566
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  📚 AI Book Summarizer with Vector Search – n8n Automation

  Overview
  This n8n workflow automates the process of summarizing uploaded books from Google Drive using vector databases and LLMs. It uses Cohere for embeddings, Qdrant for storage and retrieval, and DeepSeek or your preferred LLM for summarization and Q\&A. Designed for researchers, students, and productivity enthusiasts!



  Result Example

  Problem 🛠️

  ⏳ Reading full books or papers to extract core ideas can take hours.
  🧠 Manually summarizing or searching inside long documents is inefficient and overwhelming.

  Solution ✅

  Use this workflow to:

  Upload a book to Google Drive 📥
  Auto-split and embed the content into Qdrant 🔍
  Summarize it using DeepSeek or another LLM 🤖
  Store the final summary back to Google Drive 📤
  Clean up the vector store afterward 🧹

  🔥 Result

  ⚡ Instant AI-generated book summary
  💡 Ability to perform semantic search and question-answering
  📁 Summary saved back to your cloud
  🧠 Enhanced productivity for learning and research

  Setup ⚙️ (4–8 minutes)

  1. Google Drive Setup
  🔗 Connect Google Drive credentials
  📁 Create an input folder (e.g., book_uploads)
  📁 Create an output folder (e.g., book_summaries)
  ⚡ Trigger: Use File Created node to monitor book_uploads
  📥 Summary will be saved in book_summaries

  2. LLM & Embeddings Setup
  🔑 Create and test API keys for:
    DeepSeek/OpenAI for summarization
    Cohere for embeddings
    Qdrant for vector storage
  🧪 Ensure all credentials are added in n8n

  How It Works 🌟

  📂 A file is uploaded to Google Drive
  ⬇️ File is downloaded
  🧱 It's processed, split into chunks, and sent to Qdrant using Cohere embeddings
  ❓ A Q\&A chain with vector retriever performs information extraction
  🧠 A DeepSeek AI Agent analyzes and summarizes the book
  📤 The summary is saved to your Drive
  🧽 The Qdrant vector collection is deleted (clean-up)

  What’s Included 📦

  ✅ Google Drive integration (input/output)
  ✅ File chunking and embedding using Cohere
  ✅ Vector storage with Qdrant
  ✅ Q\&A with vector retrieval
  ✅ Summarization via DeepSeek or other LLM
  ✅ Clean-up for minimal storage overhead

  Customization 🎨

  You can tailor it to your use case:

  🧑‍🏫 Adjust summarization prompt for study notes or executive summaries
  🌍 Add translation node for multilingual support
  🔍 Enable long-term memory by skipping vector deletion
  📨 Send summaries to Notion, Slack, or Email
  🧩 Use other LLM providers (OpenAI, Claude, Gemini, etc.)

  🌐 Explore more workflows

  ❤️ Buy more workflows at: adamcrafts
  🦾 Custom workflows at: adamcrafts@cloudysoftwares.com
  adamaicrafts@gmail.com

  &gt; Build once, customize endlessly, and scale your video content like never before. 🚀

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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML","knowledge management"],"complexity":"medium","use_cases":["Automatic book/paper summarization","Semantic search and Q&A over long documents","Research note-taking and knowledge extraction","Automated content pipeline saving summaries to cloud storage","Customizable summarization workflows (study/executive)"],"estimated_setup_minutes":6,"required_services":["Google Drive","Cohere (embeddings)","Qdrant (vector DB)","DeepSeek or other LLM (OpenAI, Claude, Gemini, etc.)","n8n (workflow runtime)"],"key_features":["File-created trigger to monitor Drive uploads","File download and recursive text chunking","Embedding generation and storage in Qdrant","Vector retrieval-based Q&A chain","LLM-driven book summarization (DeepSeek/other) and prompt customization options"],"target_audience":["Researchers","Students","Productivity enthusiasts and knowledge workers」「Developers building automation/integration workflows"]}


name: 🚀Transform Podcasts into Viral TikTok Clips with Gemini+ Multi-Platform Posting✅
author_name: Matt F.
author_username: lemolex
categories: []
created_at: 2025-06-01T20:22:16.132Z
updated_at: 2025-06-01T20:22:16.132Z
url: https://n8n.io/workflows/4568
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 19:51:23
description:
  🎯 Automatically Create and Post Engaging Clips (with Audience Retention Videos) from Podcasts Using AI! 🚀

  Effortlessly transform long-form podcast content into highly engaging, viral-read clips with this end-to-end automation template.

  Designed for content creators already monetizing on TikTok/YouTube/Instagram/Twitter and those looking to start earning from the platform, this workflow streamlines the process of extracting highlights, editing clips, and posting to all your social media, allowing you to maximize reach while minimizing manual effort.

  Key Features

  🔹 AI-Powered Podcast Highlight Extraction

  Automatically identifies the best moments from any podcast video, ensuring each clip is engaging and shareable.

  🔹 Smart Video Editing & Captioning

  Combines podcast highlights with a copyright-free attention retainer video (e.g., Minecraft parkour, GTA 5 gameplay) for increased audience retention. Auto-generated captions make clips more dynamic and accessible.

  🔹 Automated Title Generation

  A Large Language Model (LLM) analyzes the clips to generate compelling titles, optimized for TikTok/YouTube/Instagram/Twitter’s algorithm.

  🔹 Hands-Free Multi Platform Posting

  Seamlessly schedules and automatically posts clips to your Soicual Media accounts at defined intervals, keeping your audience engaged without manual uploads.

  🔹 Fully Automated Workflow

  From video download to content publishing 100% FREE, this template eliminates the need for time-consuming video editing, helping you scale your content strategy effortlessly, without having to pay for multiple subscriptions tediously.
  Simply find a podcast you like and a cool Minecraft parkour (or any engaging) video, send their YouTube URLs, and let the automation handle everything—from video downloading and audio processing to highlight extraction, editing, captions, and publishing.

  How It Works (Step-by-Step Guide)

  1️⃣ Provide the YouTube URLs

  One for the main podcast video (where highlights will be extracted).
  One for the background attention retainer video (e.g., Minecraft parkour, GTA 5 gameplay).

  2️⃣ Automation Downloads and Processes the Videos

  Downloads both videos.
  Extracts audio from the podcast for analysis.

  3️⃣ AI Analyzes and Extracts Key Highlights

  Detects the most engaging moments from the podcast.

  4️⃣ Creates Fully Edited Clips

  Merges podcast highlights with the attention retainer video.
  Generates captions automatically.

  5️⃣ Optimizes for Social Media

  Uses AI to generate a compelling title for each clip.

  6️⃣ Posts to your Social Media Channels Automatically

  Uploads clips at your preferred intervals with zero manual effort.

  Who Is This For?

  ✅ Content creators already making money on TikTok/YouTube/Instagram/Twitter
  ✅ People looking to start earning with TikTok/YouTube/Instagram/Twitter
  ✅ Podcasters wanting to repurpose content into bite-sized, viral clips

  Get Started Today! 🚀

  This AI-driven automation is perfect for scaling your TikTok/YouTube/Instagram/Twitter content effortlessly.

  To use this workflow, you’ll just need free accounts on Assembly, Andynocode, and Upload-Posts.

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metadata_json:
  {"categories":["automation","social media","video processing","content repurposing","AI/ML"],"complexity":"complex","use_cases":["Convert long-form podcasts into short social clips","Automate multi-platform posting and scheduling","Generate AI-driven titles and captions","Increase audience retention using attention retainer footage","Scale content repurposing workflows for creators/agencies"],"estimated_setup_minutes":45,"required_services":["Assembly","Andynocode","Upload-Posts","YouTube (source videos)","TikTok (target)","YouTube (target)","Instagram (target)","Twitter/X (target)","Google Gemini / LLM API"],"key_features":["AI-powered podcast highlight extraction","Smart video editing that merges highlights with attention-retainer footage","Automatic caption generation","LLM-generated platform-optimized titles","Scheduled, hands-free multi-platform posting from a single workflow','End-to-end automation from download to publish (no manual editing)"],"target_audience":["Content creators (TikTok/YouTube/Instagram/Twitter)","Podcasters wanting bite-sized clips","Social media managers and small agencies looking to scale content repurposing automation"]}


name: Create AI Videos with Scripts, Images & HeyGen Avatars (🔥 LIMITED-TIME OFFER)
author_name: Adam Crafts
author_username: adamcrafts
categories: []
created_at: 2025-06-01T21:53:46.740Z
updated_at: 2025-06-01T21:53:46.740Z
url: https://n8n.io/workflows/4569
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Short Content Automation 🎬 (AI Video System with Bulk Gen, Avatar & Music Customization)
  🔥 LIMITED-TIME OFFER: AI Video Automation (Previously \$59)
  Previously Template

  🚀 Transform Your Content Game in Minutes – Save Hours of Work!
  Introducing a powerful AI Video System to automate short video creation — from scriptwriting to avatar + image generation, music, and assembly — all with n8n. This is not a basic template. It's a complete, customizable system with step-by-step videos and my personal enhancements.

  🎁 Why $10 Now? (Regular Price: \$59)

  ✅ Over 4000 people viewed my original automation — see it here — and I receive many messages from people who want it.

  ✅ I learned n8n from free templates and I want to give back.

  ✅ People asking for a more accessible price — this is my answer.

  ⏳ Limited-Time Deal: 7 Days Only!

  After that, this price goes away and the blueprint will be unlisted.

  📦 What You Get

  ✅ Full n8n Workflow: From text prompt → ready-to-publish short video
  ✅ AI Tools Integration: OpenAI, Leonardo.AI, HeyGen, etc.
  ✅ Customization Options: Avatar, Voice, Music, Style
  ✅ Video Setup Guide: Step-by-step walkthrough
  ✅ Clean UI, easy-to-use, even if you're not a developer

  💬 Made With Love by an n8n Creator

  This system is based on months of refinement and feedback from users like you.
  Now it’s your turn to scale your content, automate your work, and stand out.

  💵 Only $10 – Let’s Build Together.

  &gt; 📌 Get it now. Use it forever. No subscriptions. Just value.

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name: Email News Briefing by Keyword from Bright Data with AI Summary
author_name: Gleb D
author_username: glebd
categories: []
created_at: 2025-06-01T21:54:53.569Z
updated_at: 2025-06-01T21:54:53.569Z
url: https://n8n.io/workflows/4570
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow automatically retrieves recent Reuters news articles related to a user-specified keyword, summarizes the main findings using Google Gemini, formats the output into styled HTML, and sends a clean email report to a specified address.

  🚀 What It Does
  Collects news data from Bright Data's Reuters dataset.
  Sorts and filters top 10 most recent news articles by publication_date.
  Sends structured news data to Gemini Flash for summarization.
  Converts Gemini's response (in Markdown) into styled HTML.
  Delivers a concise news briefing via email, including clickable source links and topic highlights.

  🛠️ Step-by-Step Setup
  User Form: Accepts a keyword from the user via an n8n form trigger.
  Bright Data API: Posts a discover_new request to Bright Data's Reuters dataset using the keyword.
  Snapshot Polling: Waits and checks for dataset readiness using the snapshot ID.
  Data Retrieval: Downloads the news data once the snapshot is complete.
  Parsing: Filters and sorts the latest 10 articles using a Python Code node.
  AI Analysis: Google Gemini summarizes the filtered content into one briefing.
  Markdown → HTML: Converts AI response into styled HTML using Markdown + Code node.
  Email Delivery: Sends the briefing as an email to a predefined recipient.

  🧠 How It Works
  Polling Control: Uses Wait and If nodes to handle Bright Data snapshot readiness.
  Date Sorting: Publication dates (ISO 8601 format) are parsed and used for sorting.
  AI Summarization: Gemini condenses multiple articles into one cohesive summary.
  Formatting: Clean HTML with readable styles is generated dynamically before sending.

  📨 Final Output
  The email includes:
  A brief summary of the most important developments
  Date range of the collected news
  Topics covered

  🔐 Credentials Used
  Bright Data API (replace YOUR_API_KEY in the HTTP nodes)

  Google Gemini (Flash) API

  Email SMTP (configured in Email Send node)

  ⚠️ Notes
  You must replace all YOUR_API_KEY placeholders in Bright Data request headers with your actual Bright Data API key.
  You can customize the keyword prompt and output style freely.
  I would recommend to keep the sort = relevance option for best chronological results - sorting by date is handled later.

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metadata_json:
  {"categories":["automation","integration","data processing","AI summarization","monitoring"],"complexity":"medium","use_cases":["Daily news briefings for a keyword or topic","Media / PR monitoring","Competitive intelligence","Investor / market news alerts","Executive summaries for teams"],"estimated_setup_minutes":25,"required_services":["Bright Data Reuters dataset API (Bright Data)","Google Gemini (Flash) API","SMTP email service (any SMTP provider)","n8n instance (with Form/Webhook support)"],"key_features":["Search Reuters dataset by keyword and create a snapshot","Polling workflow to wait for dataset readiness","Filter and sort the top 10 most recent articles","Summarize multiple articles into one briefing using Google Gemini","Convert Markdown summary into styled HTML for email delivery"],"target_audience":["journalists","PR/communications teams","market analysts and researchers (investors, competitive intelligence teams)"]}


name: Retrieve Deadlock Game Match Statistics and Send to Telegram
author_name: Eska
author_username: eska
categories: []
created_at: 2025-06-01T22:19:01.820Z
updated_at: 2025-06-01T22:19:01.820Z
url: https://n8n.io/workflows/4571
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Deadlock Match Stats Bot is an automated workflow for n8n designed to send detailed player statistics from the most recent Deadlock match directly to Telegram. When the user sends the /match command to the Telegram bot, the workflow performs the following steps:

  Loads the HTML content of the player's profile page from deadlocktracker.gg using a preconfigured Steam ID.

  Extracts the most recent match ID using a regular expression from the embedded JavaScript data.

  Loads the HTML page for the specified match.

  Parses the match page using cheerio to extract relevant data for each player, including their nickname, selected hero, and current rank.

  Formats the collected information into a single message and sends it to the Telegram chat that issued the command.

nodes_used:
  ["n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.function","n8n-nodes-base.httpRequest","n8n-nodes-base.function","n8n-nodes-base.function","n8n-nodes-base.telegramTrigger"]

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metadata_json:
  {"categories":["automation","integration","data-processing","web-scraping","chatbot"],"complexity":"medium","use_cases":["Send latest Deadlock match stats to Telegram","Automate sharing of player statistics with communities","Monitor recent game matches for players or teams","Create gaming bot commands for Telegram"],"estimated_setup_minutes":15,"required_services":["deadlocktracker.gg (game stats website)","Telegram Bot API/account","n8n instance"],"key_features":["Listens for /match command from Telegram","Fetches player profile HTML and extracts latest match ID via regex","Loads match page and parses player data with cheerio","Collects nickname, hero, and rank for each player","Formats a single message and sends it back to the Telegram chat"],"target_audience":["gamers","community managers","streamers/moderators of gaming groups on Telegram\",\"developers building game-related automations"]}


name: Google Maps business scraper with contact extraction via Apify and Firecrawl
author_name: Naveen Choudhary
author_username: n8nstein
categories: []
created_at: 2025-06-02T00:09:54.996Z
updated_at: 2025-06-02T00:09:54.996Z
url: https://n8n.io/workflows/4573
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Who is this for?
  Marketing agencies, sales teams, lead generation specialists, and business development professionals who need to build comprehensive business databases with contact information for outreach campaigns across any industry.

  What problem is this workflow solving?
  Finding businesses and their contact details manually is time-consuming and inefficient. This workflow automates the entire process of discovering businesses through Google Maps and extracting their digital contact information from websites, saving hours of manual research.

  What this workflow does
  This automated workflow runs every 30 minutes to:
  Scrape business data from Google Maps using Apify's Google Places crawler
  Save basic business information (name, address, phone, website) to Google Sheets
  Filter businesses that have websites
  Scrape each business's website content using Firecrawl
  Extract contact information including emails, LinkedIn, Facebook, Instagram, and Twitter profiles
  Store all extracted data in organized Google Sheets for easy access and follow-up

  Setup
  Required Services:
  Google Sheets account with OAuth2 setup
  Apify account with API access for Google Places scraping
  Firecrawl account with API access for website scraping

  Pre-setup:
  Copy this Google Sheet
  Configure your Apify and Firecrawl API credentials in n8n
  Set up Google Sheets OAuth2 connection
  Update the Google Sheet ID in all Google Sheets nodes

  Quick Start:
  The workflow includes detailed sticky notes explaining each phase. Simply configure your API credentials and Google Sheet, then activate the workflow.

  How to customize this workflow to your needs
  Change search criteria**: Modify the Apify scraping parameters to target different business types (restaurants, gyms, salons, etc.) or locations
  Adjust schedule**: Change the trigger interval from 30 minutes to your preferred frequency  
  Add more contact fields**: Extend the extraction code to find additional contact information like WhatsApp or Telegram
  Filter criteria**: Modify the filter conditions to target businesses with specific characteristics
  Batch size**: Adjust the batch processing to handle more or fewer websites simultaneously

  Perfect for lead generation, competitor research, and building targeted marketing lists across any industry or business type.

nodes_used:
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metadata_json:
  {"categories":["automation","data_processing","integration","web_scraping","lead_generation"],"complexity":"medium","use_cases":["Build comprehensive business/contact databases","Lead generation and outbound prospecting","Competitor research and market analysis","Automating business discovery from Google Maps","Centralizing contact information in Google Sheets"],"estimated_setup_minutes":25,"required_services":["n8n","Google Sheets (with OAuth2)","Apify (Google Places crawler API access)","Firecrawl (website scraping API access)"],"key_features":["Scheduled scraping (default every 30 minutes)","Google Maps / Google Places scraping via Apify","Website content scraping via Firecrawl","Extraction of contact info (emails, LinkedIn, Facebook, Instagram, Twitter)","Filters to process only businesses with websites and customizable criteria"],"target_audience":["Marketing agencies","Sales teams","Lead generation specialists/researchers"]}


name: Hostinger Form Lead Capture & Qualification with OpenAI, Beehiiv & Google Sheets
author_name: Aitor | 1Node
author_username: aitoralonso
categories: []
created_at: 2025-06-02T08:19:46.665Z
updated_at: 2025-06-02T08:19:46.665Z
url: https://n8n.io/workflows/4575
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow provides a robust solution for Hostinger website owners looking to streamline their lead capture and qualification process. By integrating AI and popular marketing tools, it ensures efficient management of new leads.

  How it works

  This workflow automates the process of capturing leads from a Hostinger website form, qualifying them using an AI model, and syncing them with Beehiiv and Google Sheets.

  A new form submission on your Hostinger website triggers the workflow via Gmail.
  An AI model (OpenAI) extracts and qualifies relevant data from the form response, transforming it into a structured JSON format.
  The qualified lead is then added as a new subscriber to your Beehiiv newsletter.
  Finally, the lead information is appended to a Google Sheet for comprehensive tracking and record-keeping.

  Set up steps

  Create an account on Hostinger and set up a form on your website.
  Configure a Gmail trigger to capture form submissions from your Hostinger email sender.
  Ensure your email is mapped correctly for the Hostinger sender.
  Set up your OpenAI API key for the "Extract & Qualify Message Model" node.
  Create an account on Beehiiv. Obtain your Beehiiv API key and create your credentials.
  Set up a Google Sheet and configure the "insert in Sheets" node with the correct mapping.

  Approximate setup time: 15-30 minutes

  Why you need this?

  You need this workflow if you're a Hostinger website owner who wants to:

  Automate lead capture:** Say goodbye to manual lead processing and ensure no lead slips through the cracks.
  Qualify leads intelligently:** Leverage AI to instantly extract and qualify crucial lead information, saving you valuable time and improving lead quality.
  Grow your audience efficiently:** Seamlessly add qualified leads to your Beehiiv newsletter, nurturing them from the moment they express interest.
  Maintain organized records:** Automatically centralize all your lead data in a Google Sheet for easy access, analysis, and follow-up.
  Boost productivity:** Free up your time from repetitive administrative tasks and focus on growing your business.

  👉 Need Help?
  Feel free to contact us at 1 Node.
  Get instant access to a library of free resources we created.

nodes_used:
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metadata_json:
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name: Automated Expense Approval System with GPT-4, Airtable & Pinecone Vector DB
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-06-02T08:31:30.713Z
updated_at: 2025-06-02T08:31:30.713Z
url: https://n8n.io/workflows/4576
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Automate expense reviews with AI-powered CFO-level analysis. This workflow monitors Airtable expense submissions, uses GPT-4 to analyze expenses like an experienced CFO, flags suspicious expenses with detailed reasoning, and maintains comprehensive audit trails in Pinecone vector database.

  🚀 What It Does
  Smart Monitoring**: Watches Airtable for new expense submissions
  AI CFO Analysis**: GPT-4 applies financial expertise to review amounts, categories, and descriptions
  Intelligent Flagging**: Automatically identifies policy violations and suspicious patterns
  Audit Trail**: Stores all decisions in Pinecone for compliance and searchability
  Auto Updates**: Updates Airtable records with AI decisions and detailed reasoning

  🎯 Perfect For
  Finance teams needing intelligent expense oversight
  CFOs wanting to automate expense policy enforcement
  Growing companies scaling expense management
  Businesses requiring compliance documentation

  ⚙️ Key Benefits
  ✅ 99% faster expense processing vs manual review  
  ✅ CFO-level intelligence applied to every expense  
  ✅ Complete audit trail for compliance  
  ✅ Real-time fraud detection and policy enforcement  
  ✅ Detailed explanations for every decision  

  🔧 What You Need
  Airtable base with expense data (template included)
  OpenAI API for GPT-4 analysis
  Pinecone account for audit trail storage
  Basic expense submission process

  📊 Sample Results
  Input: $4,500 business class flight to Tokyo  
  AI Decision: "Flagged - Amount exceeds typical travel thresholds. Requires verification against travel policies and client justification for premium travel."

  🛠️ Setup & Support

  Quick Setup: Deploy in 60 minutes with included templates and documentation


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

  💼 Expert Support  
  LinkedIn: https://www.linkedin.com/in/yaronbeen/

  📧 Direct Help  
  Email: Yaron@nofluff.online

  Transform expense management from manual bottleneck to intelligent automation. Let AI handle policy compliance while your finance team focuses on strategy.

nodes_used:
  ["n8n-nodes-base.airtableTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","AI/ML","finance","compliance","integration"],"complexity":"complex","use_cases":["Automated expense review and approval","Policy enforcement and exceptions handling","Real-time fraud/suspicious expense detection","Maintaining auditable decision trails","Speeding up finance operations and approvals"],"estimated_setup_minutes":60,"required_services":["Airtable (expense base)","OpenAI API (GPT-4 + embeddings)","Pinecone Vector DB"],"key_features":["Watches Airtable for new expense submissions","GPT-4 CFO-level analysis of amounts, categories, descriptions","Automatic flagging of policy violations and suspicious patterns","Writes detailed decisions and reasoning back to Airtable","Stores full audit trail and embeddings in Pinecone for search/compliance"],"target_audience":["Finance teams and controllers","CFOs and accounting managers","Compliance and audit teams in SMBs/scaleups`,`Integration/automation engineers"]}


name: Automated Revenue Predictions from Stripe Data with GPT-4 and Google Sheets
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-06-02T08:35:26.321Z
updated_at: 2025-06-02T08:35:26.321Z
url: https://n8n.io/workflows/4577
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  CFO Forecasting Agent - Marketplace Listing

  Headlines (Choose Your Favorite)

  Option 1 - Direct & Professional
  "AI-Powered CFO Forecasting Agent: Automated Revenue Predictions from Stripe Data"

  Option 2 - Benefit-Focused
  "Automate Your Financial Forecasting: Daily Revenue Predictions with AI Intelligence"

  Option 3 - Action-Oriented
  "Transform Stripe Sales Data into Intelligent 3-Month Revenue Forecasts Automatically"

  Marketplace Description

  🚀 AI-Powered Financial Forecasting on Autopilot

  Turn your Stripe sales data into intelligent revenue forecasts with this comprehensive CFO Forecasting Agent. This workflow automatically analyzes your transaction history, identifies trends, and generates professional 3-month revenue predictions using OpenAI's GPT-4.

  ✨ What This Workflow Does:

  📊 Automated Data Collection**: Fetches and processes all Stripe charges daily
  🤖 AI-Powered Analysis**: Uses OpenAI GPT-4 to analyze trends and predict future revenue
  📈 Structured Forecasting**: Generates monthly forecasts with confidence levels and insights
  💾 Multi-Platform Storage**: Saves results to both Supabase database and Google Sheets
  🕒 Scheduled Execution**: Runs automatically every day to keep forecasts current
  🧠 Smart Context**: Optional Pinecone integration for historical context and improved accuracy

  🔧 Key Features:

  Daily automated execution** at 9 AM
  Structured JSON output** with forecasts, trends, and confidence levels
  Dual storage system** for data backup and easy reporting
  RAG-enabled** for enhanced forecasting with historical context
  Professional CFO-grade insights** and trend analysis

  📋 Prerequisites:

  Stripe account with API access
  OpenAI API key (GPT-4 recommended)
  Google Sheets API credentials
  Supabase account (optional)
  Pinecone account (optional, for enhanced context)

  🎯 Perfect For:

  SaaS companies tracking subscription revenue
  E-commerce businesses needing sales forecasts
  Startups requiring investor-ready financial projections
  Finance teams automating reporting workflows

  📦 What You Get:

  Complete n8n workflow with all nodes configured
  Detailed documentation and setup instructions
  Sample data structure and output formats
  Ready-to-use Google Sheets template

  💡 Need Help or Want to Learn More?

  Created by Yaron Been - Automation & AI Specialist

  📧 Support: Yaron@nofluff.online
  🎥 YouTube Tutorials: https://www.youtube.com/@YaronBeen/videos
  💼 LinkedIn: https://www.linkedin.com/in/yaronbeen/

  Get more automation tips, tutorials, and advanced workflows on my channels!

  🏷️ Tags: 
  AI, OpenAI, Stripe, Forecasting, Finance, CFO, Automation, Revenue, Analytics, GPT-4

nodes_used:
  ["@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stripe","n8n-nodes-base.code","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.supabase","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","analytics","AI"],"complexity":"medium","use_cases":["Automated revenue forecasting from transaction data","Daily financial reporting and monitoring","SaaS subscription revenue tracking and churn analysis","Investor-ready 3-month financial projections","Augmenting forecasts with historical context (RAG)"],"estimated_setup_minutes":60,"required_services":["n8n","Stripe (API access)","OpenAI (GPT-4 API key)","Google Sheets API / Google Workspace","Supabase (optional, for DB storage)","Pinecone (optional, for vector store / RAG)"],"key_features":["Scheduled daily execution (defaults to 9 AM)","Automated Stripe charges fetching and processing","GPT-4 powered trend analysis and 3-month revenue forecasts","Structured JSON output including confidence levels and insights","Dual persistence: Google Sheets and Supabase (optional) for reporting/backups"],"target_audience":["CFOs and finance teams","Startup founders and operators","SaaS and e-commerce businesses tracking revenue metrics and forecasts"]}


name: AI-Powered Asana Task Prioritization with GPT-4 and Pinecone Memory
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-06-02T08:37:29.004Z
updated_at: 2025-06-02T08:37:29.004Z
url: https://n8n.io/workflows/4578
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Replace manual task prioritization with intelligent AI reasoning that thinks like a Chief Operating Officer. This workflow automatically fetches your Asana tasks every morning, analyzes them using advanced AI models, and delivers the single most critical task with detailed reasoning - ensuring your team always focuses on what matters most.

  ✨ What This Workflow Does:

  📋 Automated Task Collection**: Fetches all assigned Asana tasks daily at 9 AM
  🤖 AI-Powered Analysis**: Uses OpenAI GPT-4 to evaluate urgency, impact, and strategic importance
  🎯 Smart Prioritization**: Identifies the #1 most critical task with detailed reasoning
  🧠 Contextual Memory**: Leverages vector database for historical context and pattern recognition
  💾 Structured Storage**: Saves prioritized tasks to PostgreSQL with full audit trail
  🔄 Continuous Learning**: Builds organizational knowledge over time for better decisions

  🔧 Key Features:

  Daily automation** with zero manual intervention
  Context-aware AI** that learns from past prioritization decisions
  Strategic reasoning** explaining why each task is prioritized
  Vector-powered memory** using Pinecone for intelligent context retrieval
  Clean structured output** with task names, priority levels, and detailed justifications
  Database integration** for reporting and historical analysis

  📋 Prerequisites:

  Asana account with API access
  OpenAI API key (GPT-4 recommended)
  PostgreSQL database
  Pinecone account (for vector storage and context)

  🎯 Perfect For:

  Operations teams managing multiple competing priorities
  Startups needing systematic task management
  Project managers juggling complex workflows
  Leadership teams requiring strategic focus
  Any organization wanting AI-driven operational intelligence

  💡 How It Works:

  Morning Automation: Triggers every day at 9 AM
  Data Collection: Pulls all relevant tasks from Asana
  AI Analysis: Evaluates each task using COO-level strategic thinking
  Context Retrieval: Searches vector database for similar past tasks
  Smart Prioritization: Identifies the single most important task
  Structured Output: Delivers priority level with detailed reasoning
  Data Storage: Saves results for reporting and continuous improvement

  📦 What You Get:

  Complete n8n workflow with all AI components configured
  PostgreSQL database schema for task storage
  Vector database setup for contextual intelligence
  Comprehensive documentation and setup guide
  Sample task data and output examples

  💡 Need Help or Want to Learn More?

  Created by Yaron Been - Automation & AI Specialist

  📧 Support: Yaron@nofluff.online
  🎥 YouTube Tutorials: https://www.youtube.com/@YaronBeen/videos
  💼 LinkedIn: https://www.linkedin.com/in/yaronbeen/

  Discover more advanced automation workflows and AI integration tutorials on my channels!

  🏷️ Tags: 
  AI, OpenAI, Asana, Task Management, COO, Prioritization, Automation, Vector Database, Operations, GPT-4

nodes_used:
  ["@n8n/n8n-nodes-langchain.vectorStorePinecone","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.asana","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.postgres","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","AI-assisted task management","integrations","knowledge management"],"complexity":"complex","use_cases":["Daily automated task prioritization","Strategic decision support (COO-level reasoning)","Operational reporting and audit trail","Continuous learning from historical decisions","Context-aware task recommendations"],"estimated_setup_minutes":90,"required_services":["Asana (API access)","OpenAI (GPT-4)","Pinecone (vector DB)","PostgreSQL","n8n"],"key_features":["Scheduled daily task fetch (9 AM)","GPT-4 driven strategic prioritization with detailed reasoning","Vector-powered contextual memory and retrieval (Pinecone)","Structured output and audit trail persisted to PostgreSQL","Continuous learning from past prioritizations (embeddings + vector store)"],"target_audience":["Operations teams","Project managers","Startup founders and leadership (COO/CEO)”，“Automation engineers and consultants"]}


name: Automated YouTube Channel Lead Generation & Email Outreach with Apify and ZeroBounce
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-06-02T08:39:46.313Z
updated_at: 2025-06-02T08:39:46.313Z
url: https://n8n.io/workflows/4579
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Stop manually checking keyword rankings and let automation do the work for you. This comprehensive SEO monitoring workflow automatically tracks your keyword positions, compares them against your target URLs, and instantly alerts your team via Slack whenever rankings change - ensuring you never miss critical SEO movements.

  ✨ What This Workflow Does:

  📊 Automated Rank Checking**: Continuously monitors keywords stored in Airtable
  🔍 Real-Time SERP Analysis**: Uses Firecrawl API to fetch current search rankings
  📈 Intelligent Comparison**: Compares current vs. previous rankings automatically
  📝 Database Updates**: Updates Airtable records with new ranking data
  🚨 Instant Alerts**: Sends Slack notifications only when rankings change
  🎯 Target URL Matching**: Specifically tracks your domain's position in search results

  🔧 Key Features:

  Trigger-based automation** that activates when Airtable data changes
  Smart rank comparison** logic that prevents false alerts
  Conditional notifications** - only alerts on actual ranking changes
  Clean data management** with automatic Airtable updates
  Team collaboration** through Slack integration
  Scalable monitoring** for unlimited keywords

  📋 Prerequisites:

  Airtable account with Personal Access Token
  Firecrawl API key for SERP data
  Slack workspace with API access
  Basic Airtable setup with keyword data

  🎯 Perfect For:

  SEO agencies managing multiple client campaigns
  Digital marketing teams tracking organic performance
  Content creators monitoring content rankings
  E-commerce businesses tracking product visibility
  Startups needing cost-effective SEO monitoring
  Any business serious about search visibility

  💡 How It Works:

  Data Collection: Fetches keywords, target URLs, and current ranks from Airtable
  SERP Analysis: Queries Firecrawl API for real-time search results
  Rank Detection: Searches results for your target URL and determines position
  Smart Comparison: Compares new ranking against stored data
  Database Update: Updates Airtable with latest ranking information
  Conditional Alert: Sends Slack notification only if ranking changed
  Team Notification: Delivers actionable ranking updates to your team

  📦 What You Get:

  Complete n8n workflow with all integrations configured
  Airtable template with proper field structure
  Firecrawl API integration setup
  Slack notification templates
  Comprehensive setup documentation
  Sample keyword data for testing

  🚀 Benefits:

  Save Hours Weekly**: Eliminate manual rank checking
  Never Miss Changes**: Get instant alerts on ranking movements
  Team Alignment**: Keep everyone informed via Slack
  Historical Tracking**: Maintain ranking history in Airtable
  Cost Effective**: Replace expensive SEO tools with automation
  Scalable Solution**: Monitor unlimited keywords effortlessly

  💡 Need Help or Want to Learn More?

  Created by Yaron Been 
  📧 Support: Yaron@nofluff.online
  🎥 YouTube Tutorials: https://www.youtube.com/@YaronBeen/videos
  💼 LinkedIn: https://www.linkedin.com/in/yaronbeen/

  Discover more SEO automation workflows and digital marketing tutorials on my channels!

  🏷️ Tags: 
  SEO, Keyword Tracking, Airtable, Slack, Firecrawl, SERP, Automation, Rank Monitoring, Digital Marketing, Search Rankings

nodes_used:
  ["n8n-nodes-base.airtableTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.airtable","n8n-nodes-base.if","n8n-nodes-base.slack","n8n-nodes-base.noOp","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:
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name: Generate Contextual YouTube Comments Automatically with GPT-4o
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-06-02T08:41:28.071Z
updated_at: 2025-06-02T08:41:28.071Z
url: https://n8n.io/workflows/4580
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Workflow Overview
  This cutting-edge n8n workflow is a powerful automation tool designed to revolutionize how content creators and marketers engage with YouTube channels. By leveraging AI and the YouTube Data API, this workflow automatically:

  Discovers New Content: 
     Monitors a specific YouTube channel
     Retrieves the latest video in real-time
     Checks for new uploads at regular intervals

  Generates Intelligent Comments:
     Uses advanced AI (OpenAI's GPT models) to analyze video metadata
     Crafts contextually relevant, human-like comments
     Ensures each comment feels organic and engaging

  Seamless Deployment:
     Automatically posts the AI-generated comment directly on the video
     Eliminates manual interaction
     Increases potential channel visibility and engagement

  Key Benefits
  🤖 Full Automation: No manual comment writing required
  💡 Smart Contextual Comments: AI understands video content
  ⏱️ Time-Saving: Instant engagement without human intervention
  📈 Potential Increased Visibility: Regular, intelligent interactions

  Setup Requirements
  YouTube Data API Credentials
     Obtain a Google Cloud API key
     Configure channel ID you want to target
     Set up OAuth2 authentication for comment posting

  OpenAI API Access
     Create an OpenAI account
     Generate an API key for comment generation
     Select preferred GPT model (GPT-4o, GPT-3.5, etc.)

  n8n Installation
     Install n8n (cloud or self-hosted)
     Import the workflow configuration
     Configure API credentials
     Set up scheduling preferences

  Potential Use Cases
  Content Creators monitoring competitor channels
  Marketing teams maintaining online presence
  Social media managers automating engagement
  Researchers tracking specific YouTube channels

  Future Enhancements
  Logging comment history
  Dynamic OAuth2 token management
  Multi-channel support
  Sentiment analysis for comment generation

  Connect With Me

  Got questions? Want to dive deeper?

  📧 Email: Yaron@nofluff.online

  🎥 YouTube: @YaronBeen

  💼 LinkedIn: Yaron Been

  **Unlock the power of AI-driven YouTube engagement – automate, optimize, and amplify your online# Automate YouTube Engagement with GPT-4o Generated Comments

  Workflow Overview
  This n8n automation leverages AI to streamline YouTube channel engagement, providing a sophisticated solution for content interaction. By combining the YouTube Data API and OpenAI's GPT-4o, the workflow:

  Intelligent Content Discovery:
     Dynamically monitors specified YouTube channels
     Real-time detection of new video uploads
     Configurable monitoring intervals

  AI-Powered Comment Generation:
     Utilizes GPT-4o for contextual analysis
     Generates nuanced, platform-appropriate comments
     Ensures authentic, relevant interactions

  Automated Engagement:
     Seamlessly posts AI-crafted comments
     Enhances channel visibility
     Reduces manual social media management

  Key Benefits
  🤖 Advanced Automation: AI-driven engagement
  💡 Contextual Intelligence: GPT-4o powered insights
  ⏱️ Efficiency Optimization: Instant, scalable interactions
  📈 Strategic Visibility: Consistent, meaningful channel presence

  Detailed Setup Instructions

  Prerequisites
  n8n instance (cloud or self-hosted)
  YouTube Data API access
  OpenAI API key
  Target YouTube channel(s)

  Configuration Steps
  YouTube Data API Setup
     Create a Google Cloud project
     Enable YouTube Data API v3
     Generate OAuth2 credentials
     Store credentials securely in n8n

  OpenAI API Configuration
     Create OpenAI account
     Generate API key
     Select GPT-4o model
     Configure API key in n8n credentials

  Workflow Customization
     Replace placeholder channel IDs
     Adjust monitoring frequency
     Customize AI prompt for comment generation
     Configure OAuth2 authentication

  Workflow Customization Options
  Modify AI prompt to match specific content styles
  Add keyword filters for video selection
  Implement multi-channel support
  Create custom engagement rules

  Potential Use Cases
  Content creator audience engagement
  Brand social media management
  Community interaction automation
  Research and monitoring

  Ethical Considerations
  Maintain transparency about AI-generated comments
  Respect platform guidelines
  Avoid spam or misleading interactions
  Ensure comments add genuine value

  Future Enhancement Roadmap
  Advanced sentiment analysis
  Multi-language support
  Engagement performance tracking
  Adaptive comment generation

  Security Best Practices
  Never hardcode API keys
  Use n8n's credential management
  Implement secure OAuth2 authentication
  Regularly rotate API credentials

  Technical Requirements
  n8n v0.220.0 or higher
  YouTube Data API v3
  OpenAI API access
  Stable internet connection

  Workflow Architecture

  [YouTube Channel Trigger]
      ⬇️
  [Fetch Latest Video]
      ⬇️
  [AI Comment Generation]
      ⬇️
  [Post Comment]

  #YouTubeAutomation #AIEngagement #ContentMarketing #SocialMediaTech #GPT4Automation #WorkflowInnovation #AIComments #DigitalMarketing

  Connect With Me
  Exploring AI-Powered Social Media Automation?

  📧 Email: Yaron@nofluff.online
  🎥 YouTube: @YaronBeen
  💼 LinkedIn: Yaron Been

  Transform your YouTube engagement with intelligent, responsible automation!



  Note: This workflow template is a starting point. Always customize and test thoroughly in your specific environment.

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metadata_json:
  {"categories":["automation","integration","social media","ai"],"complexity":"medium","use_cases":["Automatically engage with new videos from target or competitor channels","Scale social media presence with AI-generated comments","Monitor and react to uploads for community management","Research/tracking specific YouTube channels for trends"],"estimated_setup_minutes":45,"required_services":["YouTube Data API v3 (Google Cloud project) with OAuth2 credentials","OpenAI API (GPT-4o or GPT-3.5) API key","n8n instance (cloud or self-hosted)"],"key_features":["Monitors specified YouTube channel(s) for new uploads","Fetches latest video metadata in real time","Generates contextual, human-like comments using GPT-4o","Automatically posts comments via YouTube API (OAuth2)","Configurable monitoring interval and prompt customization"],"target_audience":["content creators","marketing teams","social media managers / community managers"]}


name: YouTube Video Summaries with GPT-4o, Slack Approvals & Reddit Posting
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-06-02T08:42:27.229Z
updated_at: 2025-06-02T08:42:27.229Z
url: https://n8n.io/workflows/4581
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  🚀 AI-Powered YouTube Video Summary Distributor: From Channel to Community!

  Workflow Overview
  This sophisticated n8n automation transforms YouTube content discovery into a seamless, multi-platform intelligence sharing process. By intelligently connecting YouTube RSS, AI summarization, and content distribution platforms, the workflow:

  Discovers New Content:
     Monitors YouTube channels via RSS feed
     Captures latest video uploads
     Tracks content in real-time

  AI-Powered Summarization:
     Extracts video metadata
     Generates concise, meaningful summaries
     Leverages GPT-4o for intelligent content analysis

  Intelligent Distribution:
     Logs summaries in Google Sheets
     Sends summaries to Slack for review
     Publishes approved content to Reddit

  Detailed Setup Instructions

  1. YouTube Data API Configuration
  Prerequisites
  Google Cloud Console account
  Enabled YouTube Data API v3

  Setup Steps:
  Go to Google Cloud Console
  Create a new project
  Enable YouTube Data API v3
  Create credentials (API Key)
  Store API key securely in n8n credentials
  Obtain channel RSS feed URL

  2. OpenAI API Setup
  Prerequisites
  OpenAI account
  Active API subscription

  Configuration:
  Visit OpenAI Platform
  Generate API key
  Select GPT-4o model
  Configure API key in n8n credentials
  Set up billing and usage limits

  3. Slack Integration
  Prerequisites
  Slack workspace
  Slack app permissions

  Setup Process:
  Create a Slack app in your workspace
  Configure OAuth scopes for sending messages
  Install app to workspace
  Obtain webhook or OAuth token
  Configure in n8n Slack node

  4. Reddit API Configuration
  Prerequisites
  Reddit account
  Reddit application created

  Steps:
  Go to Reddit's app preferences
  Create a new application
  Obtain client ID and secret
  Configure OAuth2 credentials in n8n
  Select target subreddit

  Workflow Customization

  Channel Modification
  Replace YouTube RSS feed URL in trigger node
  Adjust channel_id parameter
  Modify extraction logic if needed

  Subreddit Customization
  Change subreddit parameter in Reddit node
  Adjust title and text formatting

  AI Summarization Tuning
  Modify system message in Summarizer Agent
  Adjust prompt for different content types
  Implement custom filtering

  Key Customization Points
  Modify RSS feed URL
  Change target subreddit
  Adjust AI summarization prompt
  Add custom filtering logic
  Implement multi-channel support

  Technical Requirements
  n8n v0.220.0 or higher
  YouTube Data API v3
  OpenAI API access
  Slack workspace
  Reddit application
  Stable internet connection

  Potential Use Cases
  Content creator content tracking
  Research and trend analysis
  Social media content distribution
  Automated content curation
  Community engagement

  Security Considerations
  Use environment variables for API keys
  Implement proper OAuth2 authentication
  Respect platform usage guidelines
  Maintain user privacy

  Future Enhancement Roadmap
  Multi-language support
  Advanced content filtering
  Sentiment analysis integration
  Expanded platform distribution
  Customizable summarization parameters

  Workflow Visualization

  [YouTube RSS Trigger]
      ⬇️
  [Extract Channel ID]
      ⬇️
  [Fetch Video Details]
      ⬇️
  [AI Summarization]
      ⬇️
  [Google Sheets Logging]
      ⬇️
  [Slack Approval]
      ⬇️
  [Reddit Publishing]

  Hashtag Performance Boost 🚀
  #YouTubeAutomation #AIContentDistribution #WorkflowInnovation #ContentCuration #AIMarketing #DigitalMediaTech #AutomatedSummaries #CrossPlatformContent

  Connect With Me
  Ready to revolutionize your content workflow?

  📧 Email: Yaron@nofluff.online
  🎥 YouTube: @YaronBeen
  💼 LinkedIn: Yaron Been

  Transform your content strategy with intelligent, automated workflows!

  Note: Always test and customize the workflow to fit your specific use case and comply with platform guidelines.

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metadata_json:
  {"categories":["automation","integration","content curation","social media","AI"],"complexity":"complex","use_cases":["Automated YouTube monitoring and summaries","Team review workflows (Slack approvals)","Cross-platform content distribution (Reddit)","Content curation and trend research","Logging and audit of distributed summaries"],"estimated_setup_minutes":90,"required_services":["YouTube Data API v3 (Google Cloud)","OpenAI (GPT-4o) API","Slack workspace / Slack app (OAuth or webhook)","Reddit API (OAuth2 application)","Google Sheets (Google Drive)","n8n instance"],"key_features":["Monitors YouTube channels via RSS and fetches video details","Generates AI-powered summaries using GPT-4o","Logs summaries to Google Sheets for audit","Sends summaries to Slack for human approval","Publishes approved content to Reddit automatically (configurable subreddit)"],"target_audience":["Content creators","Community managers","Social media marketers/strategists`,`Automation engineers / n8n users"]}


name: Automated YouTube Channel Lead Generation & Email Outreach with Apify and ZeroBounce
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-06-02T08:43:35.648Z
updated_at: 2025-06-02T08:43:35.648Z
url: https://n8n.io/workflows/4582
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Workflow Overview
  This sophisticated n8n automation is a powerful lead generation and outreach tool designed to transform YouTube channel research into actionable marketing opportunities. By intelligently connecting multiple services and APIs, this workflow:

  Discovers Targeted Channels: 
     Scrapes YouTube channels based on specific keywords
     Extracts comprehensive channel metadata
     Identifies potential business opportunities

  Intelligent Lead Qualification:
     Filters channels with contact emails
     Validates email authenticity
     Ensures high-quality lead generation

  Personalized Outreach:
     Sends customized cold emails
     Leverages channel-specific personalization
     Automates initial contact process

  Key Benefits
  🕵️ Automated Lead Discovery: Find potential collaborators or clients
  🧠 Smart Filtering: Eliminate invalid or irrelevant leads
  📧 Personalized Outreach: Contextual, channel-specific communication
  ⏱️ Time-Saving: Eliminate manual research and email hunting

  Workflow Architecture

  🔍 Stage 1: Channel Scraping
  Apify Integration**: Scrapes YouTube channels
  Keyword-Based Search**: Target specific niches
  Metadata Extraction**: Collect channel details, emails

  🧩 Stage 2: Lead Qualification
  Email Existence Check**: Filter channels with contact info
  ZeroBounce Verification**: Validate email authenticity
  Quality Control**: Ensure only valid leads proceed

  📬 Stage 3: Personalized Outreach
  Gmail Integration**: Send customized cold emails
  Dynamic Personalization**: Use channel-specific details
  Automated Communication**: Streamline initial contact

  Potential Use Cases
  Marketing Agencies**: Find potential clients
  Influencer Marketers**: Discover collaboration opportunities
  Content Creators**: Network and expand professional connections
  Sales Teams**: Generate targeted lead lists
  Recruitment Specialists**: Identify industry professionals

  Setup Requirements
  Apify Account
     API token
     YouTube Scraper Actor
     Configured search keywords

  ZeroBounce Account
     Email verification API
     Validation credits

  Gmail Account
     OAuth2 authentication
     Configured sending profile

  n8n Installation
     Cloud or self-hosted instance
     Import workflow configuration
     Configure API credentials

  Future Enhancement Suggestions
  🤖 AI-powered email personalization
  📊 Advanced lead scoring mechanisms
  🔄 Automated follow-up sequences
  📈 Integration with CRM platforms
  🌐 Multi-platform lead generation

  Ethical Considerations
  Respect email communication guidelines
  Comply with anti-spam regulations
  Provide clear opt-out mechanisms
  Maintain professional, value-driven outreach

  Connect With Me

  Ready to supercharge your lead generation?

  📧 Email: Yaron@nofluff.online

  🎥 YouTube: @YaronBeen

  💼 LinkedIn: Yaron Been

  Transform your outreach strategy with intelligent, automated workflows!

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metadata_json:
  {"categories":["automation","lead generation","integration","marketing automation","data processing"],"complexity":"complex","use_cases":["Discover targeted YouTube channels for outreach","Validate and qualify email leads","Send personalized cold-email campaigns","Build targeted prospect lists for sales/recruiting","Integrate outreach results with CRM or workflows"],"estimated_setup_minutes":45,"required_services":["Apify (YouTube scraper actor & API token)","ZeroBounce (email verification API & credits)","Gmail (OAuth2 sending account)","n8n instance (cloud or self-hosted)"],"key_features":["Keyword-based YouTube channel scraping","Channel metadata extraction (including contact emails)","Email existence filtering and ZeroBounce validation","Dynamic, channel-specific email personalization","Automated cold email sending via Gmail OAuth2"],"target_audience":["Marketing agencies","Influencer marketers","Content creators/networkers','Sales teams','Recruiters','Growth/Outbound specialists"]}


