Workflow Templates

AI-Powered Airtable Contact Manager Overview The AI-Powered Airtable Contact Manager is an intelligent n8n workflow that enables AI assistants to seamlessly manage contact data in Airtable through natural language interactions. Using the Model Context Protocol (MCP), this workflow bridges the gap between conversational AI and structured data management. How It Works This workflow creates a powerful AI-to-database interface that allows users to manage their Airtable contacts through natural language commands. Here's the complete flow: 1. AI Interaction Layer Users interact with an AI assistant using natural language Examples: "Add John Doe to contacts", "Find all contacts assigned to Sarah", "Show me contact details for ID xyz" 2. MCP Server Trigger The AI assistant processes the user's request and identifies the needed operation Sends structured commands to the n8n workflow via the MCP (Model Context Protocol) Acts as the intelligent routing system for all contact operations 3. Airtable Operations The workflow provides four core contact management functions: 🔍 Get Record: Retrieves specific contact details using a Record ID Input: Record ID from AI Output: Complete contact information (Name, Email, Assignee, Status) ➕ Create Record: Adds new contacts to the database Input: Contact details (Name, Email, Assignee) Output: New record with auto-generated ID and default status 🗑️ Delete Record: Removes contacts permanently Input: Record ID to delete Output: Confirmation of successful deletion 🔎 Search Records: Finds contacts using flexible criteria Input: Airtable formula for filtering Output: All matching contact records 4. Smart Data Handling The workflow uses AI-powered field mapping with $fromAI() functions Automatically extracts relevant information from natural language requests Maintains data integrity with proper field validation Setup Steps Prerequisites n8n instance (cloud or self-hosted) Airtable account with API access MCP-compatible AI system Step 1: Airtable Preparation Create Airtable Base: Set up a new base or use existing one Note your Base ID (starts with app) Set Up Contact Table: Create a table with these fields: Name (Single line text) email (Email) Assignee (Single line text) Status (Single select: Todo, In progress, Done) Note your Table ID (starts with tbl) Generate API Token: Go to https://airtable.com/developers/web/api/introduction Create a personal access token with full permissions Save this token securely Step 2: n8n Configuration Import Workflow: Copy the enhanced JSON workflow Import into your n8n instance Configure Airtable Credentials: Go to Credentials in n8n Create new "Airtable Personal Access Token" credential Enter your Airtable API token Name it "full access" (or update credential references in workflow) Update Base and Table IDs: Replace YOUR_AIRTABLE_BASE_ID with your actual Base ID (starts with app) Replace YOUR_AIRTABLE_TABLE_ID with your actual Table ID (starts with tbl) Update in all four Airtable nodes Update Credential References: Replace your-airtable-credential-id with your actual credential ID Or rename your credential to match "Airtable API Token" Step 3: MCP Integration Configure MCP Server: Set up your MCP server to communicate with n8n Replace your-webhook-path-here and your-webhook-id-here with your actual webhook details Configure your AI system to use this workflow Update Node IDs (Optional): The workflow uses placeholder node IDs for privacy n8n will auto-generate new IDs when you import No action needed unless you're referencing specific nodes Test the Integration: Activate the workflow in n8n Test each operation through your AI interface Verify data flows correctly between AI and Airtable Step 4: Customization (Optional) Add More Fields: Extend the Airtable schema as needed Update the Create Record node field mappings Modify the Search Record filters Enhanced Error Handling: Add error handling nodes Set up notifications for failed operations Implement retry logic for reliability Usage Examples Once set up, users can interact with the system naturally: Creating Contacts: "Add Sarah Johnson with email sarah@company.com, assign to Mike" "Create a new contact for David Wilson, email david@startup.io" Finding Contacts: "Show me all contacts assigned to Jennifer" "Find contacts with status 'In progress'" "Search for contacts with gmail addresses" Managing Records: "Get details for contact rec123ABC" "Delete the contact with ID rec456DEF" "Update John's status to Done" Benefits Natural Language Interface**: No technical knowledge required Automated Data Entry**: Reduces manual work and errors Flexible Searching**: Find contacts using any criteria AI-Powered**: Leverages advanced language understanding Scalable**: Easily extend with more operations Integrated**: Works seamlessly with existing Airtable workflows Technical Notes Uses n8n's $fromAI() function for intelligent data extraction Implements MCP for standardized AI-to-automation communication Supports Airtable's formula syntax for complex searches Maintains security through proper credential management Designed for high reliability with error handling capabilities This workflow transforms contact management from a manual, time-consuming task into an effortless, conversational experience powered by AI.

This n8n workflows builds another example of creating a knowledgebase assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for your chatbot. In this example, a government tax code policy document is used. Whilst we could split the document into chunks by content length, we often lose the context of chapters and sections which may be required by the user. Our approach then is to first split the document into chapters and sections before importing into our vector store. Additionally, using metadata correctly is key to allow filtering and scoped queries. Example Human: "Tell me about what the tax code says about cargo for intentional commerce?" AI: "Section 11.25 of the Texas Property Tax Code pertains to "MARINE CARGO CONTAINERS USED EXCLUSIVELY IN INTERNATIONAL COMMERCE." In this section, a person who is a citizen of a foreign country or an en..." How it works The tax code policy document is downloaded as a zip file from the government website and its pages are extracted as separate chapters. Each chapter is then parsed and split into its sections using data manipulation expressions. Each section is then inserted into our Qdrant vector store tagged with its source, chapter and section numbers as metadata. When our AI Agent needs to retrieve data from our vector store, we use a custom workflow tool to perform the query to Qdrant. Because we're relying on Qdrant's advanced filtering capabilities, we perform the search using the Qdrant API rather than the Qdrant node. When the AI Agent, needs to pull full wording or extracts, we can use Qdrant's scroll API and metadata filtering to do so. This makes Qdrant behave like a key-value store for our document. Requirements A Qdrant instance is required for the vector store and specifically for it's filtering functionality. Mistral.ai account for Embeddings and AI models. Customising this workflow Depending on your use-case, consider returning actual PDF pages (or links) to the user for the extra confirmation and to build trust. Not using Mistral? You are able to replace but note to match the distance and dimension size of Qdrant collection to your chosen embedding model.

Complete MCP server exposing 2 Wayback API operations to AI agents. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Credentials Add Wayback API credentials Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works This workflow converts the Wayback API into an MCP-compatible interface for AI agents. • MCP Trigger: Serves as your server endpoint for AI agent requests • HTTP Request Nodes: Handle API calls to https://api.archive.org • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Returns responses directly to the AI agent 📋 Available Operations (2 total) 🔧 Wayback (2 endpoints) • GET /wayback/v1/available: GET /wayback/v1/available • POST /wayback/v1/available: POST /wayback/v1/available 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Path parameters and identifiers • Query parameters and filters • Request body data • Headers and authentication Response Format: Native Wayback API responses with full data structure Error Handling: Built-in n8n HTTP request error management 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Cursor: Add MCP server SSE URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n HTTP request handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Who is this for? This workflow is ideal for individuals focused on nutrition tracking, meal planning, or diet optimization—whether you’re a health-conscious individual, fitness coach, or developer working on a healthtech app. It also fits well for anyone who wants to capture their meal data via voice or text, without manually entering everything into a spreadsheet. What problem is this workflow solving? Manually logging meals and breaking down their nutritional content is time-consuming and often skipped. This workflow automates that process using Telegram for input, OpenAI for natural language understanding, and Google Sheets for structured tracking. It enables users to record meals by typing or sending voice messages, which are transcribed, analyzed for nutrients, and automatically stored for tracking and review. What this workflow does This n8n automation lets users send either a text or voice message to a Telegram bot describing their meal. The workflow then: Receives the Telegram message Checks if it’s a voice message • If yes: Downloads the audio file and transcribes it using OpenAI • If no: Uses the text input directly Sends the meal description to OpenAI to extract a structured list of ingredients and nutritional details Parses and stores the results in Google Sheets Responds via Telegram with a personalized confirmation message A testing interface also allows you to simulate prompts and view structured outputs for development or debugging. Setup Create a Telegram bot via BotFather and note the API token. Create an empty Google Sheet and store the sheet ID in the environment. Set up your OpenAI credentials in the n8n credential manager. Customize the “List of Ingredients and Nutrients” node with your prompt if needed. (Optional) Use the “Testing” section to simulate messages and refine outputs before going live. How to customize this workflow to your needs • Enhance prompts in the OpenAI node to improve the structure and accuracy of responses. • Add new fields in the Google Sheet and corresponding logic in the parser if you want more detail. • Adjust the Telegram response to provide motivational feedback, dietary tips, or summaries. • Upgrade to the “Pro” version mentioned in the contact section for USDA database integration and complete nutrient breakdowns. This is a lightweight, AI-powered meal logging automation that transforms voice or text into actionable nutrition data—perfect for making healthy eating easier and more data-driven. See my other workflows here

Proxmox AI Agent with n8n and Generative AI Integration This template automates IT operations on a Proxmox Virtual Environment (VE) using an AI-powered conversational agent built with n8n. By integrating Proxmox APIs and generative AI models (e.g., Google Gemini), the workflow converts natural language commands into API calls, enabling seamless management of your Proxmox nodes, VMs, and clusters. Buy My Book: Mastering n8n on Amazon Full Courses & Tutorials: http://lms.syncbricks.com Watch Video on Youtube How It Works Trigger Mechanism The workflow can be triggered through multiple channels like chat (Telegram, email, or n8n's built-in chat). Interact with the AI agent conversationally. AI-Powered Parsing A connected AI model (Google Gemini or other compatible models like OpenAI or Claude) processes your natural language input to determine the required Proxmox API operation. API Call Generation The AI parses the input and generates structured JSON output, which includes: response_type: The HTTP method (GET, POST, PUT, DELETE). url: The Proxmox API endpoint to execute. details: Any required payload parameters for the API call. Proxmox API Execution The structured output is used to make HTTP requests to the Proxmox VE API. The workflow supports various operations, such as: Retrieving cluster or node information. Creating, deleting, starting, or stopping VMs. Migrating VMs between nodes. Updating or resizing VM configurations. Response Formatting The workflow formats API responses into a user-friendly summary. For example: Success messages for operations (e.g., "VM started successfully"). Error messages with missing parameter details. Extensibility You can enhance the workflow by connecting additional triggers, external services, or AI models. It supports: Telegram/Slack integration for real-time notifications. Backup and restore workflows. Cloud monitoring extensions. Key Features Multi-Channel Input**: Use chat, email, or custom triggers to communicate with the AI agent. Low-Code Automation**: Easily customize the workflow to suit your Proxmox environment. Generative AI Integration**: Supports advanced AI models for precise command interpretation. Proxmox API Compatibility**: Fully adheres to Proxmox API specifications for secure and reliable operations. Error Handling**: Detects and informs you of missing or invalid parameters in your requests. Example Use Cases Create a Virtual Machine Input: "Create a VM with 4 cores, 8GB RAM, and 50GB disk on psb1." Action: Sends a POST request to Proxmox to create the VM with specified configurations. Start a VM Input: "Start VM 105 on node psb2." Action: Executes a POST request to start the specified VM. Retrieve Node Details Input: "Show the memory usage of psb3." Action: Sends a GET request and returns the node's resource utilization. Migrate a VM Input: "Migrate VM 202 from psb1 to psb3." Action: Executes a POST request to move the VM with optional online migration. Pre-Requisites Proxmox API Configuration Enable the Proxmox API and generate API keys in the Proxmox Data Center. Use the Authorization header with the format: PVEAPIToken=<user>@<realm>!<token-id>=<token-value> n8n Setup Add Proxmox API credentials in n8n using Header Auth. Connect a generative AI model (e.g., Google Gemini) via the relevant credential type. Access the Workflow Import this template into your n8n instance. Replace placeholder credentials with your Proxmox and AI service details. Additional Notes This template is designed for Proxmox 7.x and above. For advanced features like backup, VM snapshots, and detailed node monitoring, you can extend this workflow. Always test with a non-production Proxmox environment before deploying in live systems. Start with n8n Learn n8n with Amjid Get n8n Book What is Proxmox

Automated QuickBooks Invoice to Custom PDF & Email Tired of the standard, boring invoices from QuickBooks Online? This workflow completely automates the process of creating beautiful, custom-branded PDF invoices and emailing them directly to your clients, saving you time and elevating your brand's professionalism. The moment you create an invoice in QuickBooks, this workflow triggers, fetches all the necessary data, and generates a lavish, multi-page-aware PDF invoice complete with your company logo and signature. Key Features Fully Automated:** Runs instantly when a new invoice is created in QuickBooks. Custom Branding:** Automatically fetches your company logo and signature from a URL to place on the invoice. Modern & Professional Design:** Uses a premium, multi-column HTML template that is clean, easy to read, and far superior to the default QBO templates. Multi-Page Ready:** If an invoice has many line items, the template will intelligently create multiple pages and add a "Page X of Y" footer automatically. Smart Layout:** The totals and summary block are designed to never break across pages, ensuring a professional look no matter the length. Automatic Emailing:** The final PDF is attached to a beautifully formatted email and sent directly to the customer's email address on file. Prerequisites Before you start, you will need a few things: A running n8n instance. A QuickBooks Online account with API access. A running Gotenberg instance. This is a powerful, open-source tool for converting HTML to PDF. This workflow is designed to connect to its API. You can learn more about it here. Publicly accessible URLs for your company logo and signature image (e.g., hosted on your website or a service like Imgur). Setup Guide Follow these steps carefully to configure the workflow for your own use. Nodes that need your attention are marked with a [!!] prefix. Step 1: Configure the QuickBooks Webhook The workflow starts with a webhook. You need to tell QuickBooks to send information to this webhook. Open the [!!] Listen for New QuickBooks Invoice node. You will see a Webhook URL. Copy the Production URL. Go to your QuickBooks Developer dashboard, select your app, and navigate to the Webhooks section. Paste the n8n URL into the Endpoint URL field and select the Invoice event to subscribe to. Step 2: Connect Your QuickBooks Account Open the [!!] Get Invoice Data from QuickBooks node. In the "Credentials" field, select your existing QuickBooks Online credentials or create a new set. Step 3: Add Your Branding Open the [!!] Fetch Company Logo Image node. In the URL field, replace the placeholder with the public URL of your company's logo. Open the [!!] Fetch Company Signature Image node. In the URL field, replace the placeholder with the public URL of your signature image. Step 4: Update the PDF Generation Service Open the [!!] Generate PDF via Gotenberg node. In the URL field, replace the placeholder http://YourGotenBergInstanceURL/... with the real URL of your running Gotenberg instance. Step 5: Configure Your Email Open the [!!] Email PDF Invoice to Customer node. In the "Credentials" field, select your SMTP or email service credentials. Customize the From Email and Subject fields. You can also edit the beautiful HTML email body to match your company's tone of voice. Step 6: Activate Your Workflow You're all set! Save the workflow and activate it using the toggle at the top-right of the screen. Now, when you create a new invoice in QuickBooks, this automation will handle the rest. A Note from the Creator Thank you for using this workflow! I believe that professional and automated invoicing is a cornerstone of a great business. This tool was designed to save you time and help you put your best foot forward with every client interaction. If you have any questions or need assistance, feel free to reach out. Website:** https://www.elegantbiztech.com/ Email:** sales@elegantbiztech.com

One-way sync between Telegram, Notion, Google Drive, and Google Sheets Who is this for? This workflow is perfect for productivity-focused teams, remote workers, virtual assistants, and digital knowledge managers who receive documents, images, or notes through Telegram and want to automatically organize and store them in Notion, Google Drive, and Google Sheets—without any manual work. What problem is this workflow solving? Managing Telegram messages and media manually across different tools like Notion, Drive, and Sheets can be tedious. This workflow automates the classification and storage of incoming Telegram content, whether it’s a text note, an image, or a document. It saves time, reduces human error, and ensures that media is stored in the right place with metadata tracking. What this workflow does Triggers on a new Telegram message** using the Telegram Trigger node. Classifies the message type** using a Switch node: Text messages are appended to a Notion block. Images are converted to base64, uploaded to imgbb, and then added to Notion as toggle-image blocks. Documents are downloaded, uploaded to Google Drive, and the metadata is logged in Google Sheets. Sends a completion confirmation** back to the original Telegram chat. Setup Telegram Bot: Set up a bot and get the API token. Notion Integration: Share access to your target Notion page/block. Use the Notion API credentials and block ID where content should be appended. Google Drive & Sheets: Connect the relevant accounts. Select the destination folder and spreadsheet. imgbb API: Obtain a free API key from imgbb. Replace placeholder credential IDs and asset URLs as needed in the imported workflow. How to customize this workflow to your needs Change Storage Locations**: Update the Notion block ID or Google Drive folder ID. Switch Google Sheet to log in a different file or sheet. Add More Filters**: Use additional Switch rules to handle other Telegram message types (like videos or voice messages). Modify Response Message**: Personalize the Telegram confirmation text based on the file type or sender. Use a different image hosting service** if you don’t want to use imgbb.

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!

🛠️ Demio Tool MCP Server Complete MCP server exposing all Demio Tool operations to AI agents. Zero configuration needed - all 4 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Demio Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Demio Tool tool with full error handling 📋 Available Operations (4 total) Every possible Demio Tool operation is included: 📅 Event (3 operations) • Get an event • Get many events • Register an event 🔧 Report (1 operations) • Get a report 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Demio Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Demio Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

Who is this for? This workflow is ideal for developers, content creators, or customer support teams looking to automate text-to-speech conversion using OpenAI. What problem does this solve? It automates the process of converting text inputs into speech, reducing manual effort and enhancing productivity. What this workflow does: This workflow triggers when a text input is received via a webhook, converts it into audio using the OpenAI API, and sends the generated speech back through a webhook response. Setup: Ensure you have an OpenAI API key (you can get it from OpenAI website). Set up the webhook URL and parameters. Configure the OpenAI node with your API key (Create New Credentials). set up the responde to webhook node.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. AI Arena - Debate of AI Agents to Optimize Answers and Simulate Diverse Scenarios Overview Version: 1.0 The AI Arena Workflow is designed to facilitate a refined answer generation process by enabling a structured debate among multiple AI agents. This workflow allows for diverse perspectives to be considered before arriving at a final output, enhancing the quality and depth of the generated responses. ✨ Features Multi-Agent Debate Simulation**: Engage multiple AI agents in a debate to generate nuanced responses. Configurable Rounds and Agents**: Easily adjust the number of debate rounds and participating agents to fit your needs. Contextualized AI Responses**: Each agent operates based on predefined roles and characteristics, ensuring relevant and focused discussions. JSON Output**: The final output is structured in JSON format, making it easy to integrate with other systems or workflows. 👤 Who is this for? This workflow is ideal for developers, data scientists, content creators, and businesses looking to leverage AI for decision-making, content generation, or any scenario requiring diverse viewpoints. It is particularly useful for those who need to synthesize information from multiple personalities or perspectives. 💡 What problem does this solve? The workflow addresses the challenge of generating nuanced responses by simulating a debate among AI agents. This approach ensures that multiple perspectives are considered, reducing bias and enhancing the overall quality of the output. Use-Case examples: 🗓️ Meeting/Interview Simulation ✔️ Quality Assurance 📖 Storywriter Test Environment 🏛️ Forum/Conference/Symposium Simulation 🔍 What this workflow does The workflow orchestrates a debate among AI agents, allowing them to discuss, critique, and suggest rewrites for a given input based on their roles and predefined characteristics. This collaborative process leads to a more refined and comprehensive final output. 🔄 Workflow Steps Input & Setup: The initial input is provided, and the AI environment is configured with necessary parameters. Round Execution: AI agents execute their roles, providing replies and actions based on the input and their individual characteristics. Round Results: The results of each round are aggregated, and a summary is created to capture the key points discussed by the agents. Continue to Next Round: If more rounds are defined, the process repeats until the specified number of rounds is completed. Final Output: The final output is generated based on the agents' discussions and suggestions, providing a cohesive response. ⚡ How to Use/Setup 🔐 Credentials Obtain an API key for the Mistral API or another LLM API. This key is necessary for the AI agents to function properly. 🔧 Configuration Set up the workflow in n8n, ensuring all nodes are correctly configured according to the workflow requirements. This includes setting the appropriate input parameters and defining the roles of each AI agent. This workflow uses a custom node for Global Variables called 'n8n-nodes-globals.' Alternatively, you can use the 'Edit Field (Set)' node to achieve the same functionality. ✏️ Customizing this workflow To customize the workflow, adjust the AI agent parameters in the JSON configuration. This includes defining their roles, personalities, and preferences, which will influence how they interact during the debate. One of the notes includes a ready-to-use example of how to customize the agents and the environment. You can simply edit it and insert it as your credential in the Global Variables node. 📌 Example An example with both input and final output is provided in a note within the workflow. 🛠️ Tools Used n8n: A workflow automation tool that allows users to connect various applications and services. Mistral API: A powerful language model API used for generating AI responses. (You can replace it with any LLM API of your choice) Podman: A container management tool that allows users to create, manage, and run containers without requiring a daemon. (It serves as an alternative to Docker for container orchestration.) ⚙️ n8n Setup Used n8n Version**: 1.100.1 n8n-nodes-globals**: 1.1.0 Running n8n via**: Podman 4.3.1 Operating System**: Linux ⚠️ Notes, Assumptions & Warnings Ensure that the AI agents are configured with clear roles to maximize the effectiveness of the debate. Each agent's characteristics should align with the overall goals of the workflow. The workflow can be adapted for various use cases, including meeting simulations, content generation, and brainstorming sessions. This workflow assumes that users have a basic understanding of n8n and JSON configuration. This workflow assumes that users have access to the necessary API keys and permissions to utilize the Mistral API or other LLM APIs. Ensure that the input provided to the AI agents is clear and concise to avoid confusion in the debate process. Ambiguous inputs may lead to unclear or irrelevant outputs. Monitor the output for relevance and accuracy, as AI-generated content may require human oversight to ensure it meets standards and expectations before being used in production. ℹ️ About Us This workflow was developed by the Hybroht team of AI enthusiasts and developers dedicated to enhancing the capabilities of AI through collaborative processes. Our goal is to create tools that harness the possibilities of AI technology and more.

📌 AI Agent Template with Bright Data MCP Tool Integration This template enables natural-language-driven automation using Bright Data MCP tools. It extracts all available tools from MCP, processes the user’s query through an AI agent, then dynamically selects and executes the appropriate tool. ❓ Problem It Solves Traditional automation often requires users to understand APIs, interfaces, or scripts to perform backend tasks. The Bright Data MCP integration solves this by allowing natural language interaction, intelligently classifying user intent, and managing context-aware execution of complex operations—ideal for data extraction, customer support, and workflow orchestration. 🧰 Pre-requisites Before deploying this template, make sure you have: An active N8N instance (self-hosted or cloud). A valid OpenRouter API key (or another compatible AI model). Telegram bot and its API token Access to the Bright Data MCP API with credentials. Basic familiarity with N8N workflows and nodes. ⚙️ Setup Instructions Setup and obtain API token and other necessary information from Bright Data In your Bright Data account, obtain the following information: API token Web Unlocker zone name (optional) Browser Zone name (optional) Host SSE server from STDIO command The methods below will allow you to receive SSE (Server-Sent Events) from Bright Data MCP via a local Supergateway or Smithery ** Method 1: Run Supergateway in a separate web service (Recommended) This method will work for both cloud version and self-hosted N8N. Signup to any cloud services of your choice (DigitalOcean, Heroku, Hetzner, Render, etc.). For NPM based installation: Create a new web service. Choose Node.js as runtime environment and setup a custom server without repository. In your server’s settings to define environment variables or .env file, add: `API_TOKEN=your_brightdata_api_token WEB_UNLOCKER_ZONE=optional_zone_name BROWSER_ZONE=optional_browser_zone_name` Paste the following text as a start command: npx -y supergateway --stdio "npx -y @brightdata/mcp" --port 8000 --baseUrl http://localhost:8000 --ssePath /sse --messagePath /message Deploy it and copy the web server URL, then append /sse into it. Your SSE server should now be accessible at: https://your_server_url/sse For Docker based installation: Create a new web service. Choose Docker as the runtime environment. Set up your Docker environment by pulling the necessary images or creating a custom Dockerfile. In your server’s settings to define environment variables or .env file, add: `API_TOKEN=your_brightdata_api_token WEB_UNLOCKER_ZONE=optional_zone_name BROWSER_AUTH=optional_browser_auth` Use the following Docker command to run Supergateway: `docker run -it --rm -p 8000:8000 supercorp/supergateway \ --stdio "npx -y @brightdata/mcp /" \ --port 8000` Deploy it and copy the web server URL, then append /sse into it. Your SSE server should now be accessible at: https://your_server_url/sse For more installation guides, please refer to https://github.com/supercorp-ai/supergateway.git. ** Method 2: Run Supergateway in the same web service as the N8N instance This method will only work for self-hosted N8N. a. Set Required Environment Variables In your server's settings to define environment variables or .env file, add: API_TOKEN=your_brightdata_api_token WEB_UNLOCKER_ZONE=optional_zone_name BROWSER_ZONE=optional_browser_zone_name b. Run Supergateway in Background npx -y supergateway --stdio "npx -y @brightdata/mcp" --port 8000 --baseUrl http://localhost:8000 --ssePath /sse --messagePath /message Use the command above to execute it through the cloud shell or set it as a pre-deploy command. Your SSE server should now be accessible at: http://localhost:8000/sse For more installation guides, please refer to https://github.com/supercorp-ai/supergateway.git. * *Method 3: Configure via Smithery.ai* (Easiest) If you don't want additional setup and want to test it right away, follow these instructions: Visit https://smithery.ai/server/@luminati-io/brightdata-mcp/tools to: Signup (if you are new to Smithery) Create an API key Define environment variables via a profile Retrieve your SSE server HTTP URL Import the Workflow Open N8N. Import the JSON workflow file included with this template. Update any nodes referencing external services (e.g., OpenRouter, Telegram). Setup Telegram Integration If you haven't setup a bot in Telegram, below is the instruction how to create one using BotFather: Search for @BotFather in Telegram and start a conversation with it. Send the command /newbot to create a new bot. You'll be prompted to enter a name and a unique username for your bot. BotFather will provide you with an access token, which you'll need to use to interact with the bot's API. Edit the HTTP Request node in the workflow. Configure the URL as follows: https://api.telegram.org/bot+your_telegram_bot_token+/setWebhook?url=+your_webhook_url Replace +your_telegram_bot_token+ with your actual Telegram bot token. Replace +your_webhook_url+ with the URL from the Webhook Trigger node in the workflow. This will set up Telegram to forward messages to your n8n agent. 🔄 Workflow Functionality (Summary) The user submits a message via chat. Memory** nodes retain context for multi-turn conversations. The mapped tool is executed and results are returned contextually. 🧠 Optional memory buffers and memory manager nodes keep the interaction context-aware. 🧩 Use Cases Data Scraping on Demand**: Launch scraping tasks via chat. Lead Generation Bots**: Enrich or validate leads with MCP tools. AI-Powered Customer Support**: Classify and answer queries with real-time data tools. Workflow Assistants**: Let teams run backend processes like lookups or report generation using plain language. 🛠️ Customization Classifier Prompt & Logic**: Tweak the AI’s prompt and tool-matching schema to better fit your use case. Memory Configuration**: Adjust retention policies and context depth. Tool Execution Sub-Workflow**: Extend for retries, logging, or chaining actions. Omni-Channel Support**: Connect via webhooks to chat interfaces like Slack, WhatsApp, Telegram, or custom UIs. ✅ Summary This template equips you with a powerful no-code/low-code AI agent that translates conversation into real-world action. Using Bright Data’s MCP tools through natural language, it enables teams to automate and scale data-driven tasks effortlessly.