Workflow Templates

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.

This n8n template from Intuz delivers a complete and automated solution to streamline your development workflow for a single repository. By embedding specific keywords and a JIRA issue ID within your git commit commands, this workflow automatically creates a Pull Request in GitHub and simultaneously updates the corresponding JIRA ticket. This provides a complete, seamless integration that eliminates manual steps and keeps your project management perfectly in sync with your codebase. How it works This workflow acts as a powerful bridge between your Git repository and your project management tools, driven entirely by the structure of your commit messages. GitHub Webhook Trigger: The workflow starts when a developer pushes a new commit to a specified repository in GitHub. Parse Commit Message: A Code node extracts key information from the commit message: The JIRA Issue Key (e.g., FF-1196). The base branch for the PR (e.g., development). Action commands like [auto-pr] and [taskcompleted]. Conditional PR Creation: An IF node checks if the [auto-pr] command is present. If yes, it uses the GitHub node to automatically create a pull request from the developer's branch to the specified base branch. If no, this step is skipped, allowing for multiple commits before a PR is made. Conditional JIRA Update: Another IF node checks for the [taskcompleted] command. If yes, it uses the JIRA node to transition the corresponding issue to your "Done" status (e.g., "Task Completed" or "In Review"). If no, the JIRA issue remains in its current state, perfect for work-in-progress commits. How to Use: Quick Start Guide Click the "Use Template" button to import this workflow into your n8n instance. Configure the GitHub Trigger: Open the "GitHub Push Trigger" node. It will display a unique Webhook URL. Copy this URL. In your GitHub repository, go to Settings > Webhooks > Add webhook. Paste the URL into the Payload URL field. Set the Content type to application/json. Under "Which events would you like to trigger this webhook?", select Just the push event. Click Add webhook. Connect Your Accounts: GitHub: Select your GitHub API credential in the "Create Pull Request" node. JIRA : Select your JIRA API credential in the "Update JIRA Issue Status" node. Customize the JIRA Transition (Important): Open the "Update JIRA Issue Status" node. In the Transition parameter, you need to set the specific status you want to move the issue to (e.g., 'Done', 'Completed', 'In Review'). You can use the ID or the exact name of the transition from your JIRA project's workflow. Activate the Workflow: Save your changes and activate the workflow. You're ready to automate! Example Commit Message: git commit -m "FF-1196 Implement OAuth login [auto-pr,development,taskcompleted]" Key Requirements to Use Template An active n8n instance. A GitHub account with repository admin permissions to create webhooks. A JIRA Cloud account with permissions to update issues. Developers who can follow the specified git commit message format. Connect with us Website: https://www.intuz.com/cloud/stack/n8n Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz

🔍 Yelp Business Finder: Scraping Local Businesses by Keyword, Category & Location Using Bright Data and Google Sheets Description: Automate local business data collection from Yelp using AI-powered input validation, Bright Data scraping, and automatic Google Sheets integration. Perfect for market research, lead generation, and competitive analysis. 🛠️ How It Works Form Submission: Users submit a simple form with country, location, and business category parameters. AI Validation: Google Gemini AI validates and cleans input data, ensuring proper formatting and Yelp category alignment. Data Scraping: Bright Data's Yelp dataset API scrapes business information based on the cleaned parameters. Status Monitoring: The workflow monitors scraping progress and waits for data completion. Data Export: Final business data is automatically appended to your Google Sheets for easy analysis. 📋 Setup Steps ⏱️ Estimated Setup Time: 10-15 minutes Prerequisites ✅ Active n8n instance (cloud or self-hosted) ✅ Google account with Sheets access ✅ Bright Data account with Yelp scraping dataset ✅ Google Gemini API access Configuration Steps Import Workflow: Copy the provided JSON workflow In n8n: Go to Workflows → + Add workflow → Import from JSON Paste the JSON and click Import Configure Google Sheets: Create a new Google Sheet or use an existing one Set up OAuth2 credentials in n8n Update the Google Sheets node with your document ID Configure column mappings for business data Setup Bright Data: Add your Bright Data API credentials to n8n Replace BRIGHT_DATA_API_KEY with your actual API key Verify your Yelp dataset ID in the HTTP request nodes Test the connection Configure Google Gemini: Add your Google Gemini API credentials Test the AI Agent connection Verify the model configuration Test & Activate: Activate the workflow using the toggle switch Test with sample data: country="US", location="New York", category="restaurants" Verify data appears correctly in your Google Sheet 📊 Data Output 📍 Business Name Official business name from Yelp ⭐ Overall Rating Average customer rating (1-5 stars) 📝 Reviews Count Total number of customer reviews 🏷️ Categories Business categories and tags 🌐 Website URL Official business website 📞 Phone Number Contact phone number 📍 Address Full business address 🔗 Yelp URL Direct link to Yelp listing 🎯 Use Cases 🔍 Market Research Analyze local business landscapes and competition 📈 Lead Generation Build prospect lists for B2B outreach 🏪 Location Analysis Research business density by area and category 📊 Competitive Intelligence Monitor competitor ratings and customer feedback ⚠️ Important Notes: Ensure you comply with Yelp's terms of service and rate limits Bright Data usage may incur costs based on your plan AI validation helps improve data quality and reduce errors Monitor your Google Sheets for data accuracy 🔧 Troubleshooting Common Issues: API Rate Limits:** Implement delays between requests if needed Invalid Categories:** AI agent helps standardize category names Empty Results:** Verify location spelling and category alignment Authentication Errors:** Check all API credentials and permissions 🚀 Ready to start scraping Yelp business data efficiently!

Generate and send MadKudu Account Brief into Outreach This workflow generates an account brief tailored to your company using MadKudu MCP and OpenAI and syncs it to a custom field in Outreach. Its for Sales who want to give reps rich account context right inside Outreach, and draft Outreach email with Outreach Revenue Agent based on MadKudu account brief. ✨ Who it's for RevOps or GTM teams using MadKudu + Salesforce + Outreach Sales teams needing dynamic, AI-generated context for target accounts 🔧 How it works 1. Select Accounts: Use a Salesforce node to define which accounts to brief. Filter logic can be updated to match ICP or scoring rules (e.g., MadKudu Fit + LTB). 2. Generate Brief with MadKudu MCP & AI MadKudu MCP provides the account brief instructions, research online for company recent news and provides structured account context from your integrations connected to MadKudu + external signals (firmographics, past opportunities, active contacts, job openings...) The AI agent (OpenAI model) turns this into a readable account brief. 3. Send to Outreach Match account in Outreach via domain. Update a custom field (e.g., custom49) with the brief text. 📋 How to set up Connect your Salesforce account Used to pull accounts that need a brief. Set your OpenAI credentials Required for the AI Agent to generate the brief. Create a n8n Variable to store your MadKudu API key named madkudu_api_key used for the MadKudu MCP tool The AI Agent pulls the account brief instructions and all the context necessary to generate the briefs. Create an Oauth2 API credential to connect your Outreach account Used to sync to brief to Outreach. Customize the Salesforce filter In the “Get accounts” node, define which accounts should get a brief (e.g. Fit > 90). Map your Outreach custom field Update the JSON Body request with your actual custom field ID (e.g. custom49). 🔑 How to connect Outreach In n8n, add a new Oauth2 API credential and copy the callback URL Now go to Outreach developer portal Click “Add” to create a new app In Feature selection add Outreach API (OAuth) In API Access (Oauth) set the redirect URI to the n8n callback Select the following scopes accounts.read, accounts.write Save in Outreach Now enter the Outreach Application ID into n8n Client Id and the Outreach Application Secret into n8n Client secret Save in n8n and connect via Oauth your Outreach Account ✅ Requirements MadKudu account with access to API Key Salesforce Oauth Outreach Admin permissions to create an app OpenAI API Key 🛠 How to customize the workflow Change the targeting logic** Edit the Salesforce filter to control which accounts are eligible. Rewrite the prompt** Tweak the prompt in the AI Agent node to adjust format, tone, or insights included in the brief. Change the Outreach account field** Update the Outreach field where the brief is sync-ed if you're using a different custom field (e.g. custom48, custom32, etc). Use a different trigger** Swap the manual trigger for a Schedule or Webhook to automate the flow end-to-end.

📌 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.

📌 AI Agent Template with Bright Data MCP Tool Integration This template obtains all the possible tools from Bright Data MCP, process this through chatbot, then run any tool based on the user's query ❓ Problem It Solves The problem that the MCP solves is the complexity and difficulty of traditional automation, where users need to have specific knowledge of APIs or interfaces to trigger backend processes. By allowing interaction through natural language, automatically classifying and routing queries, and managing context and memory effectively, MCP simplifies complex data operations, customer support, and workflow orchestration scenarios where inputs and responses change dynamically. 🧰 Pre-requisites Before deploying this template, ensure you have: An active n8n instance (self-hosted or cloud). A valid OpenAI API key (or any AI models) Access to Bright Data MCP API with credentials. Basic familiarity with n8n workflows and nodes. ⚙️ Setup Instructions **Install the MCP Community Node in N8N In your N8N self-hosted instance, go to Settings → Community Nodes. Search and install n8n-nodes-mcp. Configure Credentials: Add your OpenAI API key or any AI mdeols to the relevant nodes. If you want other AI model, please replace all associated nodes of OpenAI in the workflow Set up Bright Data MCP client credentials in the installed community node (STDIO) Obtain your API in Bright Data and put it in Environment field in the credentials window. It should be written as API_Key=<your api key from Bright Data> 🔄 Workflow Functionality (Summary) User message** triggers the workflow. AI Classifier** (OpenAI) interprets the intent and maps it to a tool from Bright Data MCP. If no match is found, the user is notified. If more information is needed, the AI requests it. Memory** preserves context for follow-up actions. The tool is executed, and results are returned contextually to the user. > 🧠 Optional memory buffer and chat memory manager nodes keep conversations context-aware across multiple messages. 🧩 Use Cases Data Scraping Automation**: Trigger scraping tasks via chat. Lead Generation Bots**: Use MCP tools to fetch, enrich, or validate data. Customer Support Agents**: Automatically classify and respond to queries with tool-backed answers. Internal Workflow Agents**: Let team members trigger backend jobs (e.g., reports, lookups) by chatting naturally. 🛠️ Customization Tool Matching Logic**: Modify the AI classifier prompt and schema to suit different APIs or services. Memory Size and Retention**: Adjust memory buffer size and filtering to fit your app’s complexity. Tool Execution**: Extend the "Execute the tool" sub-workflow to handle additional actions, fallback strategies, or logging. Frontend Integration**: Connect this with various platforms (e.g., WhatsApp, Slack, web chatbots) using the webhook. ✅ Summary This template delivers a powerful no-code/low-code agent that turns chat into automation, combining AI intelligence with real-world tool execution. With minimal setup, you can build contextual, dynamic assistants that drive backend operations using natural language.

This n8n automation lets you build a complete AI-powered task management system that integrates Telegram, Google Sheets, and GPT-4o mini to help users easily manage to-do lists and receive daily task reminders. Users can interact with the system via Telegram, while the AI assistant (powered by GPT-4o mini) processes commands naturally, updates a central Google Sheet, and ensures scheduled reminders are sent for pending tasks. ✨ Key Features ✅ Add, list, update, complete, or delete tasks via Telegram ✅ AI-powered conversational responses using GPT-4o ✅ All tasks stored and synced in Google Sheets ✅ Daily scheduled task summary and pending reminders sent to Telegram ✅ Friendly, human-like assistant responses ✅ Fully configurable and easy to set up 🛠️ Workflow Functionality Breakdown 1. User Interacts on Telegram Sends commands like: add buy groceries list tasks complete submit report delete dentist appointment 2. AI-Powered Processing A GPT-4o agent processes user messages Ensures clear, friendly responses Determines task intent: add, update, delete, list, complete 3. Google Sheets Sync Every operation is logged to Google Sheets Google Sheets acts as the master task database Sheet structure includes: Task Status (pending or done) Created At (timestamp) Due Date (optional) Notes (optional) 4. Scheduled Daily Task Summary At 9 PM daily, the workflow: Fetches tasks from Google Sheets Generates a warm, conversational summary via GPT-4o Sends the summary to the user on Telegram 5. Automated Reminders Checks for pending tasks due today Sends reminder messages to Telegram ✅ Prerequisites Before setting up the workflow, ensure you have: ✔️ An n8n instance (Cloud or self-hosted) ✔️ A Telegram Bot Token ✔️ Access to Google Sheets API (OAuth2 credentials) ✔️ An OpenAI API Key with GPT-4o access ✔️ A Google Sheet structured as per the specification below 📝 Google Sheet Structure Your Google Sheet should have the following columns: | Column Name | Description | | -------------- | ---------------------------------------------------- | | Task | Short task description | | Status | pending or done | | Created At | Date & time task was created (YYYY-MM-DD HH:mm:ss) | | Due Date | (Optional) When task is due (YYYY-MM-DD HH:mm:ss) | | Notes | (Optional) Additional details | Important: The first row should be the header row with these exact column names. 🔧 Setup Instructions 1. Telegram Bot Setup Create a bot via BotFather Obtain the Bot Token Connect Telegram Trigger and Telegram Send nodes using your Bot Token in n8n 2. Google Sheets API Setup Follow n8n Google Sheets integration guide Set up OAuth2 credentials Provide access to your task Google Sheet 3. OpenAI API Setup Obtain an API key from OpenAI Ensure GPT-4o mini access is enabled Add OpenAI credentials to relevant nodes 4. Sheet Linking Replace the Google Sheet ID in the workflow with your own Confirm sheet names and column structure match exactly 5. Schedule Configuration (Optional) Adjust the daily summary time (Schedule Trigger node) as desired ⚙️ Configuration Options 🔧 Adjust AI prompt instructions for tone/style 🔧 Change reminder times in the schedule trigger 🔧 Customize Google Sheet columns if needed (update mappings accordingly) 🔧 Add multi-user support with chat ID checks (advanced) 📂 Files Included Full n8n JSON workflow ready to import 💡 Tips You can extend this with Slack, WhatsApp, or Email reminders Combine with Notion, ClickUp, or CRM integrations for more powerful task management Consider adding a "Priority" column for advanced sorting Ready to stay organized with AI-powered task management? Import this workflow, link your accounts, and your Telegram assistant is good to go! 🚀

Multi-Agent AI Healthcare Assistant Demo ⚠️ EDUCATIONAL DEMONSTRATION ONLY - NOT FOR PRODUCTION MEDICAL USE ⚠️ A comprehensive demonstration of n8n's advanced multi-agent AI orchestration capabilities, showcasing how to build sophisticated conversational AI systems with specialized agent coordination. 🎯 What This Demo Shows Advanced Multi-Agent Architecture: Main Orchestrator Agent** - Traffic controller and decision maker Patient Registration Agent** - Specialized data collection and validation Appointment Scheduler Agent** - Complex multi-step booking workflows Medical Report Analyzer** - Document processing and analysis Prescription Medicine Analyzer** - Medicine verification and safety checks Technical Learning Objectives: Multi-agent coordination patterns Conditional agent routing and tool selection Memory management across conversations Multi-modal input processing (text, audio, images, documents) Complex state management in AI workflows External system integration (Google Sheets, WhatsApp, OpenAI) 🏗️ Architecture Highlights Multi-Modal Processing Pipeline: Text Messages** → Direct agent processing Audio Messages** → Transcription → Text processing → Audio response Images** → Vision analysis → Context integration Documents** → PDF extraction → Content analysis Agent Specialization: Each agent has focused responsibilities and constraints Intelligent document classification and routing Context-aware tool selection Error handling and recovery mechanisms Memory & State Management: Session-based conversation persistence Context sharing between specialized agents Multi-step workflow state tracking 🔧 Technical Implementation Key n8n Features Demonstrated: @n8n/n8n-nodes-langchain.agent - Main orchestrator @n8n/n8n-nodes-langchain.agentTool - Specialized sub-agents @n8n/n8n-nodes-langchain.memoryPostgresChat - Conversation memory n8n-nodes-base.googleSheetsTool - External data integration Complex conditional logic and routing Integration Patterns: WhatsApp Business API integration OpenAI GPT-4 model orchestration Google Sheets as data backend PostgreSQL for conversation memory Multi-step document processing 📚 Learning Value For n8n Developers: Enterprise-grade workflow architecture patterns AI agent orchestration best practices Complex conditional logic implementation Memory management in conversational AI Multi-modal data processing techniques Error handling and recovery strategies For AI Engineers: Agent specialization and coordination Tool calling and function integration Context management across conversations Multi-step workflow design Production workflow considerations ⚙️ Setup Requirements Required Credentials: OpenAI API Key (GPT-4 access recommended) WhatsApp Business API credentials Google Sheets OAuth2 API PostgreSQL database connection External Dependencies: Google Sheets database (template structure provided) WhatsApp Business Account PostgreSQL database for conversation memory 🚨 Important Disclaimers Educational Use Only: This is a DEMONSTRATION of n8n capabilities NOT suitable for actual medical use** NOT HIPAA compliant** Use only with fictional/test data Production Considerations: Requires proper security implementation Needs compliance review for medical use Consider HIPAA-compliant alternatives for healthcare Implement proper data encryption and access controls 🎓 Educational Applications Perfect for Learning: Advanced n8n workflow patterns Multi-agent AI system design Complex automation architecture Integration pattern best practices Conversational AI development Workshop & Training Use: AI automation workshops n8n advanced training sessions Multi-agent system demonstrations Integration pattern tutorials 🔄 Workflow Components Main Flow: WhatsApp message reception and media processing Input classification and routing Main agent orchestration and tool selection Specialized agent execution Response formatting and delivery Sub-Agents: Registration Tool** - Patient data collection Scheduler Tool** - Appointment booking logic Report Analyzer** - Medical document analysis Medicine Analyzer** - Prescription verification 💡 Customization Ideas Extend the Demo: Add more specialized agents Implement different communication channels Integrate with other healthcare APIs Add more sophisticated document processing Implement advanced analytics and reporting Adapt for Other Industries: Customer service automation Educational assistance systems E-commerce support workflows Technical support orchestration 🎯 Perfect for: Learning advanced n8n patterns, AI system architecture, multi-agent coordination ⏱️ Setup Time: 30-45 minutes (with credentials) 📈 Skill Level: Intermediate to Advanced 🏷️ Tags: AI Agents, Multi-Agent Systems, Healthcare Demo, Educational, Advanced Workflows

Effortlessly integrate NixGuard API into your n8n workflows for real-time security insights using your API key. This connector enables seamless interaction with Nix, providing rapid Retrieval-Augmented Generation (RAG) event knowledge with Wazuh integration - completely free and set up in under 5 minutes! 🚀 Features: ✅ Query NixGuard's AI-driven security insights via API authentication ✅ Real-time security event knowledge integration ✅ Plug-and-play workflow trigger for effortless automation ✅ Wazuh compatibility for full security visibility 🛠 How to Use: 1️⃣ Add your API Key to authenticate with NixGuard. 2️⃣ Integrate with your existing n8n workflows using the workflow trigger (default enabled). 3️⃣ (Optional) Activate the chat trigger to streamline security queries via chat-based inputs. 4️⃣ Run the workflow and get instant security intelligence! 📢 Perfect for: Startup CTO's, SOC teams, security engineers, and developers needing real-time security automation within their infrastructure. 🔗 Learn more about NixGuard: thenex.world 🔗 Get started with a free security subscription: thenex.world/security/subscribe

Workflow Overview This workflow automates the process of updating a Spotify playlist with tracks from a YouTube playlist, ensuring no duplicates are added. Key Components Manual Trigger: Starts the workflow when you click ‘Test workflow’. Spotify Integration: Retrieves tracks from a specified Spotify playlist. YouTube Integration: Fetches tracks from a designated YouTube playlist. Batch Processing: Processes tracks in batches to handle multiple items efficiently. Track Search: Searches for YouTube tracks on Spotify to find corresponding IDs. Comparison: Compares existing Spotify tracks with YouTube tracks to identify which ones to add. Track Addition: Adds new Spotify tracks to the playlist that are not already included. If you have any questions or need clarification, feel free to ask!

Free template for voice & text messages with short-term memory This n8n workflow template is a blueprint for an AI Telegram bot that processes both voice and text messages. Ready to use with minimal setup. The bot remembers the last several messages (10 by default), understands commands and provides responses in HTML. You can easily swap GPT-4 and Whisper for other language and speech-to-text models to suit your needs. Core Features Text: send or forward messages Voice: transcription via Whisper Extend this template by adding LangChain tools. Requirements Telegram Bot API OpenAI API (for GPT-4 and Whisper) 💡 New to Telegram bots? Check our step-by-step guide on creating your first bot and setting up OpenAI access. Use Cases Personal AI assistant Customer support automation Knowledge base interface Integration hub for services that you use: Connect to any API via HTTP Request Tool Trigger other n8n workflows with Workflow Tool

This workflow automates the process of retrieving Hugging Face paper summaries, analyzing them with OpenAI, and storing the results in Notion. Here’s a breakdown of how it works: ⏰ Scheduled Trigger: The flow is set to run automatically at 8 AM on weekdays. 📄 Fetching Paper Data: It fetches Hugging Face paper summaries using their API. 🔍 Data Check: Before processing, the workflow checks if the paper already exists in Notion to avoid duplicates. 🤖 Content Analysis with OpenAI: If the paper is new, it extracts the summary and uses OpenAI to analyze the content. 📥 Store Results in Notion: After analysis, the summarized data is saved in Notion for easy reference. ⚙️ Set Up Steps for Automation Follow these steps to set up this automated workflow with Hugging Face, OpenAI, and Notion integration: 🔑 Obtain API Tokens: You’ll need the Notion and OpenAI API tokens to authenticate and connect these services with n8n. 🔗 Integration in n8n: Link Hugging Face, OpenAI, and Notion by configuring the appropriate nodes in n8n. 🔧 Configure Workflow Logic: Set up a cron trigger for automatic execution at 8 AM on weekdays. Use an HTTP request node to fetch Hugging Face paper data. Add logic to check if the data already exists in Notion. Set up the OpenAI integration to analyze the paper’s summary. Store the results in Notion for easy access and reference. Result: