Workflow Templates

CallForge - AI Gong Transcript PreProcessor Transform your Gong.io call transcripts into structured, enriched, and AI-ready data for better sales insights and analytics. Who is This For? This workflow is designed for: ✅ Sales teams looking to automate call transcript formatting. ✅ Revenue operations (RevOps) professionals optimizing AI-driven insights. ✅ Businesses using Gong.io that need structured, enriched call transcripts for better decision-making. What Problem Does This Workflow Solve? Manually processing raw Gong call transcripts is inefficient and often lacks essential context for AI-driven insights. With CallForge, you can: ✔ Extract and format Gong call transcripts for structured AI processing. ✔ Enhance metadata using sales data from Salesforce. ✔ Classify speakers as internal (sales team) or external (customers). ✔ Identify external companies by filtering out free email domains (e.g., Gmail, Yahoo). ✔ Enrich customer profiles using PeopleDataLabs to identify company details and locations. ✔ Prepare transcripts for AI models by structuring conversations and removing unnecessary noise. What This Workflow Does 1. Retrieves Gong Call Data Calls the Gong API to extract call metadata, speaker interactions, and collaboration details. Fetches call transcripts for AI processing. 2. Processes and Cleans Transcripts Converts call transcripts into structured, speaker-based dialogues. Assigns each speaker as either Internal (Sales Team) or External (Customer). 3. Extracts Company Information Retrieves Salesforce data** to match customers with existing sales opportunities. Filters out free email domains* to determine the *customer’s actual company domain**. Calls the PeopleDataLabs API to retrieve additional company data and location details. 4. Merges and Enriches Data Combines Gong metadata, Salesforce customer details and insights**. Ensures all necessary data is available for AI-driven sales insights. 5. Final Formatting for AI Processing Merges all call transcript data into a single structured format for AI analysis. Extracts the final cleaned, enriched dataset for further AI-powered insights. How to Set Up This Workflow 1. Connect Your APIs 🔹 Gong API Access – Set up your Gong API credentials in n8n. 🔹 Salesforce Setup – Ensure API access if you want customer enrichment. 🔹 PeopleDataLabs API – Required to retrieve company and location details based on email domains. 🔹 Webhook Integration – Modify the webhook call to push enriched call data to an internal system. CallForge - 01 - Filter Gong Calls Synced to Salesforce by Opportunity Stage CallForge - 02 - Prep Gong Calls with Sheets & Notion for AI Summarization CallForge - 03 - Gong Transcript Processor and Salesforce Enricher CallForge - 04 - AI Workflow for Gong.io Sales Calls CallForge - 05 - Gong.io Call Analysis with Azure AI & CRM Sync CallForge - 06 - Automate Sales Insights with Gong.io, Notion & AI CallForge - 07 - AI Marketing Data Processing with Gong & Notion CallForge - 08 - AI Product Insights from Sales Calls with Notion How to Customize This Workflow 💡 Modify Data Sources – Connect different CRMs (e.g., HubSpot, Zoho) instead of Salesforce. 💡 Expand AI Analysis – Add another AI model (e.g., OpenAI GPT, Claude) for advanced conversation insights. 💡 Change Speaker Classification Rules – Adjust internal vs. external speaker logic to match your team’s structure. 💡 Filter Specific Customers – Modify the free email filtering logic to better fit your company’s needs. Why Use CallForge? 🚀 Automate Gong call transcript processing to save time. 📊 Improve AI accuracy with enriched, structured data. 🛠 Enhance sales strategy by extracting actionable insights from calls. Start optimizing your Gong transcript analysis today!

Automating LinkedIn Company Page Discovery Use Case Finding the official LinkedIn page of a company is crucial for tasks like outreach, research, or enrichment. This automation streamlines the process by intelligently searching the company’s website and LinkedIn to locate the correct profile. What This Automation Does This automation identifies a company's LinkedIn page using the following input parameters: Company domain**: The official website domain of the company (e.g., company.com). Airtop Profile (connected to LinkedIn)**: The name of your Airtop Profile authenticated on LinkedIn. How It Works Launches an Airtop session using the provided authenticated profile. Attempts to extract a LinkedIn link directly from the company's website. If not found, performs a LinkedIn search for the company. If still unsuccessful, falls back to a Google search. Validates the most likely result to confirm it’s a LinkedIn company page. Outputs the verified LinkedIn URL. Setup Requirements Airtop API Key — free to generate. An Airtop Profile logged in to LinkedIn (requires one-time login). Next Steps Combine with People Enrichment**: Use this with LinkedIn profile discovery for full contact + company data workflows. CRM Integration**: Automatically enrich company records with LinkedIn links. Build Custom Outreach Workflows**: Connect company pages to SDR tooling for deeper research and engagement. Read more about how to find Linkedin Company page

📝 DESCRIPTION OF THE WORKFLOW This workflow connects Salesforce and Geotab to streamline fleet tracking for field service jobs (Work Orders). When a new Work Order is created in Salesforce (with a 'New' status and valid coordinates), it creates a circular geofence zone in Geotab and updates the Work Order with the zone ID. If geolocation is missing, an alert email is sent to dedicated email. The workflow uses a Salesforce Outbound Message to trigger an n8n webhook. It includes robust credential handling and optional logic to skip or notify on bad data. Use Cases: Automating vehicle geofence setup for service visits Enhancing CRM-to-fleet system synchronisation Enforcing work orders data quality via alerts Integrations Used: Salesforce Geotab API Microsoft Outlook (or any SMTP-compatible service) Tags: geotab, salesforce, fleet management, gps tracking, field service, crm, automation, webhook, integration ADDITIONAL RESOURCES 🔗 Salesforce Salesforce Login \[Salesforce Setup (Admin Console)]\(https://login.salesforce.com/ → click “Setup” gear icon) Outbound Messages Documentation Salesforce Developer Documentation Salesforce Workbench (API Testing Tool) 🔗 Geotab Geotab Login (MyGeotab) Geotab Developer Portal Geotab API Explorer Geotab SDK (JavaScript Samples) Geotab Support Centre

AI Comprehensive Research on User's Query with Gemini and Web Search What is this? Perform comprehensive research on a user's query by dynamically generating search terms, querying the web using Google Search (by Gemini) , reflecting on the results to identify knowledge gaps, and iteratively refining its search until it can provide a well-supported answer with citations. (like Perplexity) This workflow is a reproduction of gemini-fullstack-langgraph-quickstart in N8N. The gemini‑fullstack‑langgraph‑quickstart is a demo by the Google‑Gemini team that showcases how to build a powerful full‑stack AI agent using Gemini and LangGraph How It Works Generate Query 💬 generates one or more search queries tasks based on the User's question. uses Gemini 2.0 Flash Web Research 🌐 execute web search tasks using the native Google Search API tool in combination with Gemini 2.0 Flash. Reflection 📚 Identifies knowledge gaps and generates potential follow-up queries. Setup Configure API Credentials: Create Google Gemini(PaLM) Api Credential using you own Gemini key Connect the credential with three nodes: Google Gemini Chat Model and GeminiSearch and reflection Configure Redis Source: prepare a Redis service that can be accessed by n8n Create Redis Crediential and connect it with all Redis node Customize Try using different Gemini models. Try modifying the parameters number_of_initial_queries and max_research_loops. Why use Redis? Use Redis as an external storage to maintain global variables (counter, search results, etc.) This workflow contains a loop process, which need global variables (as State in LangGraph). It is difficult to achieve global variables management without external storage in n8n.

Enable smart, real-time answers in your WhatsApp groups using a custom webhook, Pinecone vector database, and no Facebook Business setup. > 🟡 Note: This template uses a custom WhatsApp webhook. It does not use the official WhatsApp Business API. 👥 Who is this for? This workflow is designed for individuals and teams who want to enable smart WhatsApp group automation — without going through Meta’s official WhatsApp Business API. Ideal for small businesses, internal teams, communities, and personal power users. ❓ What problem is this solving? Setting up WhatsApp bots with intelligent responses often requires approval from Meta and a verified business account. This workflow removes those barriers by using a self-hosted webhook to handle incoming messages and respond using a document-trained AI via Pinecone. ⚙️ What this workflow does Connects a regular WhatsApp number to a custom webhook Adds the bot to any group chat (it stays silent unless mentioned) Indexes documents from Google Drive into Pinecone Responds with intelligent, context-aware answers from your custom knowledge base Auto-updates its knowledge every minute as the document changes 🛠️ Setup Step 1: Connect Google Drive Set up your Google Drive credentials in n8n Step 2: Configure Pinecone Create an index in Pinecone Dimension: 1536 Select this index in both Pinecone nodes Click Test Workflow to ingest your document into Pinecone Step 3: Get Access to the WhatsApp Webhook Fill out this form to request access You’ll receive a WhatsApp confirmation for linking Step 4: Test WhatsApp Integration ✅ One-on-one test: Send a message from another number 👥 Group test: Add the bot to a group; it will only respond when tagged 🧩 How to customize this workflow Modify the system prompt inside the AI agent node to control tone and behavior Update the connected Google Doc to match your specific domain (e.g. FAQs, SOPs, product manuals) Adjust the Pinecone sync frequency if you want updates more or less often 📚 Use cases Customer Support**: Instant, intelligent replies in WhatsApp without live agents Team Knowledge Bot**: Tag the bot for quick access to SOPs and internal docs Community Groups**: Automate common questions while keeping noise low Personal AI Assistant**: A WhatsApp chatbot trained on your notes and files 📝 Sticky Note Suggestion 💬 What this template does: > Enables an AI bot in your WhatsApp group that answers questions based on a Google Doc you provide. It uses a custom webhook, Google Drive, and Pinecone. 🔧 Requirements: > Google Drive account > Pinecone account with an index (dimension 1536) > Access to the custom WhatsApp webhook (see setup steps)

Trustpilot Insights Scraper: Auto Reviews via Bright Data + Google Sheets Sync Overview A comprehensive n8n automation that scrapes Trustpilot business reviews using Bright Data and automatically stores structured data in Google Sheets. Workflow Architecture 1. 📝 Form Trigger Node Purpose: Manual input interface for users Type**: n8n-nodes-base.formTrigger Configuration**: Form Title: "Website URL" Field: "Trustpilot Website URL" Function**: Accepts Trustpilot URL input from users to initiate the scraping process 2. 🌐 HTTP Request (Trigger Scraping) Purpose: Initiates scraping on Bright Data platform Type**: n8n-nodes-base.httpRequest Method**: POST Endpoint**: https://api.brightdata.com/datasets/v3/trigger Configuration**: Query Parameters: dataset_id: gd_lm5zmhwd2sni130p include_errors: true limit_multiple_results: 2 Headers: Authorization: Bearer BRIGHT_DATA_API_KEY Body: JSON with input URL and 35+ custom output fields Custom Output Fields The workflow extracts the following data points: Company Information**: company_name, company_logo, company_overall_rating, company_total_reviews, company_about, company_email, company_phone, company_location, company_country, company_category, company_id, company_website Review Data**: review_id, review_date, review_rating, review_title, review_content, review_date_of_experience, review_url, date_posted Reviewer Information**: reviewer_name, reviewer_location, reviews_posted_overall Review Metadata**: is_verified_review, review_replies, review_useful_count Rating Distribution**: 5_star, 4_star, 3_star, 2_star, 1_star Additional Fields**: url, company_rating_name, is_verified_company, breadcrumbs, company_other_categories 3. ⌛ Snapshot Progress Check Purpose: Monitors scraping job status Type**: n8n-nodes-base.httpRequest Method**: GET Endpoint**: https://api.brightdata.com/datasets/v3/progress/{{ $json.snapshot_id }} Configuration**: Query Parameters: format=json Headers: Authorization: Bearer BRIGHT_DATA_API_KEY Function**: Receives snapshot_id from previous step and checks if data is ready 4. ✅ IF Node (Status Check) Purpose: Determines next action based on scraping status Type**: n8n-nodes-base.if Condition**: $json.status === "ready" Logic**: If True: Proceeds to data download If False: Triggers wait cycle 5. 🕒 Wait Node Purpose: Implements polling delay for incomplete jobs Type**: n8n-nodes-base.wait Duration**: 1 minute Function**: Pauses execution before re-checking snapshot status 6. 🔄 Loop Logic Purpose: Continuous monitoring until completion Flow**: Wait → Check Status → Evaluate → (Loop or Proceed) Prevents**: API rate limiting and unnecessary requests 7. 📥 Snapshot Download Purpose: Retrieves completed scraped data Type**: n8n-nodes-base.httpRequest Method**: GET Endpoint**: https://api.brightdata.com/datasets/v3/snapshot/{{ $json.snapshot_id }} Configuration**: Query Parameters: format=json Headers: Authorization: Bearer BRIGHT_DATA_API_KEY 8. 📊 Google Sheets Integration Purpose: Stores extracted data in spreadsheet Type**: n8n-nodes-base.googleSheets Operation**: Append Configuration**: Document ID: 1yQ10Q2qSjm-hhafHF2sXu-hohurW5_KD8fIv4IXEA3I Sheet Name: "Trustpilot" Mapping: Auto-map all 35+ fields Credentials: Google OAuth2 integration Data Flow User Input (URL) ↓ Bright Data API Call ↓ Snapshot ID Generated ↓ Status Check Loop ↓ Data Ready Check ↓ Download Complete Dataset ↓ Append to Google Sheets Technical Specifications Authentication Bright Data**: Bearer token authentication Google Sheets**: OAuth2 integration Error Handling Includes error tracking in Bright Data requests Conditional logic prevents infinite loops Wait periods prevent API rate limiting Data Processing Mapping Mode**: Auto-map input data Schema**: 35+ predefined fields with string types Conversion**: No type conversion (preserves raw data) Setup Requirements Prerequisites Bright Data Account: Active account with API access Google Account: With Sheets API enabled n8n Instance: Self-hosted or cloud version Configuration Steps API Keys: Configure Bright Data bearer token OAuth Setup: Connect Google Sheets credentials Dataset ID: Verify correct Bright Data dataset ID Sheet Access: Ensure proper permissions for target spreadsheet Environment Variables BRIGHT_DATA_API_KEY: Your Bright Data API authentication token Use Cases Business Intelligence Competitor analysis and market research Customer sentiment monitoring Brand reputation tracking Data Analytics Review trend analysis Rating distribution studies Customer feedback aggregation Automation Benefits Scalability**: Handle multiple URLs sequentially Reliability**: Built-in error handling and retry logic Efficiency**: Automated data collection and storage Consistency**: Standardized data format across all scrapes Limitations and Considerations Rate Limits Bright Data API has usage limitations 1-minute wait periods help manage request frequency Data Volume Limited to 2 results per request (configurable) Large datasets may require multiple workflow runs Compliance Ensure compliance with Trustpilot's terms of service Respect robots.txt and rate limiting guidelines Monitoring and Maintenance Status Tracking Monitor workflow execution logs Check Google Sheets for data accuracy Review Bright Data usage statistics Regular Updates Update API keys as needed Verify dataset ID remains valid Test workflow functionality periodically Workflow Metadata Version ID**: dd3afc3c-91fc-474e-99e0-1b25e62ab392 Instance ID**: bc8ca75c203589705ae2e446cad7181d6f2a7cc1766f958ef9f34810e53b8cb2 Execution Order**: v1 Active Status**: Currently inactive (requires manual activation) Template Status**: Credentials setup completed For any questions or support, please contact: Email or fill out this form

Complete AI Graphic Design Suite with OpenAI, Replicate & Google Drive Categories: AI Agents, Design Automation, Business Tools This workflow creates a complete AI-powered graphic design system that replaces expensive designers with intelligent automation. Featuring a conversational AI agent that orchestrates 5 specialized design tools, this suite can generate logos, style guides, gradients, and revisions on demand. Built by someone who's scaled automation agencies to $72K/month, this system demonstrates how AI agents can deliver real business value beyond simple chatbots. Benefits Complete Design Automation** - Generate logos, style guides, gradients, and revisions through natural conversation Conversational AI Interface** - Chat-based interaction makes design accessible to non-designers Professional Quality Output** - Uses advanced AI models and proven templates for consistent results Instant Delivery** - Generate designs in seconds vs. days of traditional design processes Scalable Business Tool** - Deploy for clients, embed on websites, or use internally Cost-Effective Solution** - Replace $82K/year designers with $30/month automation How It Works AI Agent Orchestration: Central conversational AI that understands design requests in natural language Automatically selects the right tool based on user needs and context Maintains conversation memory for iterative design improvements Provides professional, helpful responses with design expertise Logo Generation System: Creates professional logos using OpenAI's advanced image generation Supports various styles: minimalistic, corporate, creative, and industry-specific Automatically uploads to Google Drive with shareable links Perfect for startups, rebranding projects, and client work Style Guide Creation: Generates comprehensive brand guidelines using template-based approach Includes color palettes, typography, logo usage, and brand elements Uses AI to customize templates with client-specific information Delivers presentation-ready style guides for professional use Gradient Background Generator: Creates beautiful background gradients for websites and marketing materials Uses proven design templates with AI-powered customization Generates multiple variations and color combinations Perfect for landing pages, social media, and brand materials Design Editor & Revision System: Intelligently revises existing designs based on feedback Handles both Google Drive files and external image URLs Maintains design consistency while implementing requested changes Supports iterative improvements and client feedback cycles Advanced Upscaling Integration: Uses Replicate API to enhance image quality up to 4x resolution Professional print-quality output for all generated designs Seamlessly integrates with all design generation tools Perfect for high-resolution marketing materials and presentations Required Setup Configuration OpenAI API Setup: Connect your OpenAI API for: GPT-4 conversation handling and design guidance DALL-E (4o) image generation for all design tools Intelligent prompt processing and tool selection Google Drive Integration: Create template files for style guides and examples Set up OAuth credentials for file management Configure sharing permissions for client access Organize folders for different design categories Replicate API Configuration: Set up account for image upscaling capabilities Replace <your-replicate-api-key-here> with actual API key Configure upscaling factors (2x or 4x options) AI Agent System Message: Configure the agent with business context: You are a helpful, intelligent design assistant. You generate high-quality designs using the provided tools (generate logo, generate style guide, and generate gradient background). Then you can also upscale them, and finally, you can revise them. When you receive an image from a tool, wrap it in nice looking Markdown (atx) format and present it to the user. The only things you can generate are logos, style guides, and gradient backgrounds. Make sure to clarify which (as well as any additional information needed) so the prompt you send to the image model is optimal. If you are asked to adjust or revise an image, ask the user to define their changes as explicitly as possible. Chat Integration Options: Embedded website chat widget for client-facing design services Direct chat interface for internal team use Hosted chat endpoint for external integrations Business Use Cases Design Agencies** - Offer automated design services with instant delivery and unlimited revisions Marketing Teams** - Generate brand assets, social media graphics, and campaign materials on demand Startups** - Create professional branding without expensive design budgets Consultants** - Provide design services as value-added offerings to clients Web Developers** - Offer integrated design services alongside development projects E-commerce Businesses** - Generate product graphics, banners, and promotional materials Revenue Potential This system transforms design service economics: Replace $82K/year designers** with $30/month automation costs Instant delivery advantage** - complete designs in minutes vs. days Unlimited revisions** without additional designer time costs Premium service offering** - charge $1,500-5,000 per client implementation Scalable white-label solution** for agencies and consultants 24/7 availability** for time-sensitive client requests Difficulty Level: Intermediate Estimated Build Time: 2-3 hours Monthly Operating Cost: ~$30 (OpenAI + Replicate APIs) Watch My Complete Build Process Want to see exactly how I built this entire AI design system from scratch? I walk through the complete development process, including AI agent setup, tool integration, and the business strategy behind replacing expensive designers with intelligent automation. 🎥 Watch My Live Build: "This AI Agent Replaces an $82k/yr Graphic Designer (N8N)" This comprehensive tutorial shows the real development approach - including agent design patterns, tool orchestration, and the exact prompting strategies that deliver professional-quality results. Set Up Steps Core AI Agent Configuration: Set up chat trigger with embedded and hosted options Configure OpenAI chat model with design-focused system prompts Add memory buffer for conversation context and design iterations Design Tool Integration: Configure all 5 specialized design workflows as callable tools Set up proper data flow between agent and design generators Test tool selection logic with various design requests Template and Asset Management: Upload design templates to Google Drive for style guide generation Configure file sharing permissions for client access Set up organized folder structure for different design types Quality Control Setup: Test complete design workflows from request to delivery Validate AI output quality across all design categories Optimize prompts and templates based on actual usage patterns Client Integration Options: Embed chat widget on client websites for design services Set up hosted endpoints for external system integration Configure branding and messaging for client-facing interactions Advanced Extensions Scale the system with additional capabilities: Industry-Specific Templates** - Customize design styles for different verticals Brand Consistency Engine** - Maintain design standards across all generated assets Client Portal Integration** - Automated design delivery with approval workflows Multi-Language Support** - Generate designs with international text and cultural considerations Advanced Analytics** - Track design performance and client satisfaction metrics Print Production Tools** - Generate print-ready files with proper color profiles and dimensions Why This System Works The competitive advantage lies in intelligent automation combined with professional quality: Natural conversation interface** eliminates design tool complexity Template-based generation** ensures consistent, professional results Instant iteration capability** allows real-time design improvements Cost advantage** enables competitive pricing while maintaining margins 24/7 availability** provides service levels impossible with human designers Scalable delivery** handles multiple clients simultaneously without quality degradation Check Out My Channel For more advanced AI automation systems that generate real business results, explore my YouTube channel where I share the exact strategies used to build successful automation agencies and scale to $72K+ monthly revenue.

AI Medical Symptom Checker & Health Assistant A responsible, privacy-focused health information assistant that provides general health guidance while maintaining strict safety protocols and medical disclaimers. ⚠️ IMPORTANT DISCLAIMER This tool provides general health information only and is NOT a substitute for professional medical advice, diagnosis, or treatment. Always consult qualified healthcare providers for medical concerns. 🚀 Key Features Safety First Emergency Detection**: Automatically identifies emergency situations Immediate Escalation**: Provides emergency numbers for critical cases Clear Disclaimers**: Every response includes medical disclaimers No Diagnosis**: Never attempts to diagnose conditions Professional Referral**: Always recommends consulting healthcare providers Core Functionality Symptom Information**: General information about common symptoms Wellness Guidance**: Health tips and preventive care Medication Reminders**: General medication information Multi-Language Support**: Serve diverse communities Privacy Protection**: No data storage, anonymous processing Resource Links**: Connects to trusted health resources 🎯 Use Cases General Health Information: Learn about symptoms and conditions Pre-Appointment Preparation: Organize questions for doctors Wellness Education: General health and prevention tips Emergency Detection: Immediate guidance for critical situations Health Resource Navigation: Find appropriate care providers 🛡️ Safety Protocols Emergency Keywords Detection Chest pain, heart attack, stroke Breathing difficulties Severe bleeding, unconsciousness Allergic reactions, poisoning Mental health crises Response Guidelines Never diagnoses conditions Never prescribes medications Always includes disclaimers Encourages professional consultation Provides emergency numbers when needed 🔧 Setup Instructions Configure OpenAI API Add your API key Set temperature to 0.3 for consistency Review Legal Requirements Check local health information regulations Customize disclaimers as needed Implement required data policies Emergency Contacts Update emergency numbers for your region Add local health resources Include mental health hotlines Test Thoroughly Verify emergency detection Check disclaimer display Test various symptom queries 💡 Example Interactions General Symptom Query: User: "I have a headache for 3 days" Bot: Provides general headache information, self-care tips, when to see a doctor Emergency Detection: User: "Chest pain, can't breathe" Bot: EMERGENCY response with immediate action steps and emergency numbers Wellness Query: User: "How can I improve my sleep?" Bot: General sleep hygiene tips and healthy habits information 🏥 Integration Options Healthcare Websites**: Embed as support widget Telemedicine Platforms**: Pre-consultation tool Health Apps**: General information module Insurance Portals**: Member resource Pharmacy Systems**: General drug information 📊 Compliance & Privacy HIPAA Considerations**: No PHI storage GDPR Compliant**: No personal data retention Anonymous Processing**: Session-based only Audit Trails**: Optional logging for compliance Data Encryption**: Secure transmission 🚨 Limitations Cannot diagnose medical conditions Cannot prescribe treatments Cannot replace emergency services Cannot provide specific medical advice Should not delay seeking medical care 🔒 Best Practices Always maintain clear disclaimers Never minimize serious symptoms Encourage professional consultation Keep information general and educational Update emergency contacts regularly Review and update health information Monitor for misuse Maintain audit trails where required 🌍 Customization Options Add local emergency numbers Include regional health resources Translate to local languages Integrate with local health systems Add specific disclaimers Customize for specific populations Start providing responsible health information today!

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 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.

🔍 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!