Workflow Templates

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

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.

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! 🚀

Chat with local LLMs using n8n and Ollama This n8n workflow allows you to seamlessly interact with your self-hosted Large Language Models (LLMs) through a user-friendly chat interface. By connecting to Ollama, a powerful tool for managing local LLMs, you can send prompts and receive AI-generated responses directly within n8n. Use cases Private AI Interactions Ideal for scenarios where data privacy and confidentiality are important. Cost-Effective LLM Usage Avoid ongoing cloud API costs by running models on your own hardware. Experimentation & Learning A great way to explore and experiment with different LLMs in a local, controlled environment. Prototyping & Development Build and test AI-powered applications without relying on external services. How it works When chat message received: Captures the user's input from the chat interface. Chat LLM Chain: Sends the input to the Ollama server and receives the AI-generated response. Delivers the LLM's response back to the chat interface. Set up steps Make sure Ollama is installed and running on your machine before executing this workflow. Edit the Ollama address if different from the default.

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

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

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

AI Chatbot with Conditional Execution for Cost Efficiency Description This n8n workflow implements an AI-powered chatbot that only runs when a chat is initiated on a website. By introducing a conditional step, the workflow ensures that AI tokens are not consumed unnecessarily, making it a cost-efficient and resource-optimized solution. The chatbot, named Sophia, serves as an interactive assistant for SyncBricks. It helps users with guest posting services, YouTube review videos, IT consultancy, and online courses while collecting user details step by step. The chatbot ensures that inquiries are properly logged and confirmed before proceeding to AI-driven responses. This template is ideal for businesses, service providers, and content creators who want to optimize AI token usage while delivering personalized, interactive engagement with their users. Features Conditional Execution – The AI chatbot only activates when a chat is initiated, avoiding unnecessary API calls. AI-Powered Conversations – Uses Google Gemini AI to generate human-like responses. Step-by-Step Data Collection – Ensures structured user input, requesting name, email, and request type sequentially. Memory Buffer for Context Awareness – Maintains conversation context using a window buffer memory system. Multiple Service Offerings – Supports inquiries related to: Guest Posting Services YouTube Review Videos Online Courses on Udemy IT Consultancy Services Automated Confirmation Messages – After collecting user details, sends a confirmation message summarizing the request. How It Works Chat Message Trigger The workflow starts only when a chat message is received from the website. This ensures no AI token is consumed unless a user initiates a chat. Condition Check: Is Chat Input Provided? The workflow checks if chat input is non-empty. If the chat input is empty, the workflow stops, ensuring no unnecessary API usage. If a message is detected, the chatbot continues processing. AI-Powered Chat Response The chatbot, Sophia, generates personalized responses using Google Gemini AI. AI ensures structured conversation flow by collecting: User’s Full Name Email ID Request Type Memory Buffer for Context Retention A Window Buffer Memory system stores chat history and retrieves previous responses to ensure context-aware conversations. Response Optimization Checks memory to avoid asking the same question twice. If details are already provided, Sophia moves directly to processing the request. Confirmation & User Engagement After collecting the required details, Sophia summarizes the request as follows: "Got it [Name], your request is [Request Type]. I will be sending the details to your email ID: [Email]. Hold on while I send confirmation." Final Confirmation Message Ensures the user receives a proper acknowledgment of their inquiry. Prerequisites Before using this workflow, make sure you have: n8n Instance (Cloud or Self-Hosted) Google Gemini API Key (For AI-generated responses) Webhook Integration (To trigger the chatbot from your website) Use Cases Businesses & Enterprises – AI-powered lead qualification for services. Bloggers & Content Creators – Automated guest post inquiry handling. YouTube Influencers & Educators – AI chatbot to promote courses and review services. Marketing Agencies – Lead generation chatbot without excessive AI token consumption. E-Commerce & Consulting Services – AI-driven personalized customer engagement. Nodes Used in This Workflow Chat Trigger (Webhook) – Initiates only when a user sends a chat message. Conditional Check (If Node) – Ensures AI is only used when a chat is initiated. AI Agent (Google Gemini AI) – Generates intelligent chatbot responses. Memory Buffer (Context Retention) – Stores user inputs for context-aware conversations. Important Start with n8n Learn n8n with Amjid Get n8n Book What is Proxmox Creator Information Developed by: Amjid Ali Website: SyncBricks Email: amjid@amjidali.com LinkedIn: Amjid Ali YouTube: SyncBricks Support & Contributions If you find this workflow helpful, consider supporting my work: Donate via PayPal For full courses on n8n, visit: Course by Amjid Final Thoughts This n8n workflow ensures optimal AI token usage while engaging users with an intelligent chatbot. By integrating conditional execution, it prevents unnecessary API calls, making it cost-effective and efficient for businesses looking to automate chat-based customer interactions. Let me know if you need any modifications!

This workflow is your ultimate solution for reliable image retrieval from any web source, including those heavily protected. It operates with a smart, cost-effective strategy: it first attempts to fetch the image using a Classic Image Getter node (a standard, free HTTP request). In approximately 80% of cases, this method will be sufficient. However, for the remaining instances where you encounter IP blocking, CAPTCHAs, rate limiting, or other advanced anti-bot measures, the workflow seamlessly switches to a robust BrightData Web Unblocker service as a fallback. It leverages BrightData’s Image Unblocker to retrieve these blocked images. This template is indispensable for anyone needing consistent and complete access to web images, ensuring you get the data you need without unnecessary overhead. Why Use This Image Scrapper Workflow? Maximum Success Rate**: Retrieves images even from the most challenging or protected websites. Cost-Optimized Strategy**: Prioritizes free, standard HTTP requests, only incurring costs when advanced unblocking is truly necessary. Automated Resilience**: Intelligently handles failed direct attempts by automatically engaging the BrightData failover via the "Unlock Image" node. Versatile Image Scraping**: Perfect for market research, content aggregation, or data enrichment that demands reliable image access. How It Works When clicking ‘Execute workflow’: The workflow is initiated manually, allowing for easy testing and integration into larger processes. image: A Set node defines the target image URL. This can be easily configured to accept dynamic URLs from preceding nodes. Classic Image Getter: This HTTP Request node performs a direct image download. It's the primary, free, and efficient method for readily accessible images. Unlock Image (BrightData Web Unblocker): Configured as an error handler and failover, this HTTP Request node activates only if the "Classic Image Getter" encounters an error. It then routes the image URL through BrightData's Web Unblocker, designed to bypass advanced protective measures and successfully retrieve the image data. 🔑 Prerequisites To enable the advanced capabilities of this workflow, specifically the BrightData Web Unblocker functionality, you will need a BrightData account and a correctly configured Web Unblocker zone. Setting Up Your BrightData Web Unblocker: BrightData Account: Ensure you have an active account with BrightData. If you don't, you can sign up on their website. Create a Web Unblocker Zone: Log in to your BrightData dashboard. Navigate to the "Proxy & Scraping Infrastructure" section, then "Zones." Click "Add new zone." Select "Web Unblocker" as the product type. Give your zone a clear name (e.g., n8n-image-unlocker). Confirm the creation of the zone. Retrieve API Key: Once your Web Unblocker zone is active, go to its settings. Locate your API Key (often referred to as "password" for proxy access) within the "Access Parameters" or "Credentials" section. Configure in n8n: In the Unlock Image HTTP Request node within this workflow, update the Authorization header. Replace "Bearer yourkey" with "Bearer YOUR_BRIGHTDATA_API_KEY". Important: For production workflows, it's highly recommended to use n8n credentials to store your BrightData API key securely, rather than hardcoding it directly in the node. This template uses a placeholder for demonstration purposes. Crucially, ensure that the zone parameter in the Unlock Image node matches the exact Zone ID you created in your BrightData account. You will need to replace the placeholder web_unlocker with your actual BrightData zone ID. Phil | Inforeole

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:

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!