Workflow Templates

🚀 AI Lead Generation and Follow-Up Template 📋 Overview This n8n workflow template automates your lead generation and follow-up process using AI. It captures leads through a form, enriches them with company data, classifies them into different categories, and sends appropriate follow-up sequences automatically. Key Features: 🤖 AI-powered lead classification (Demo-ready, Nurture, Drop) 📊 Automatic lead enrichment with company data 📧 Intelligent email responses and follow-up sequences 📅 Automated demo scheduling for qualified leads 📝 Complete lead logging in Google Sheets 💬 AI assistant for immediate query responses 🛠️ Prerequisites Before setting up this workflow, ensure you have: n8n Instance: Self-hosted or cloud version OpenAI API Key: For AI-powered features Google Workspace Account with: Gmail access Google Sheets Google Calendar Basic understanding of your Ideal Customer Profile (ICP) ⚡ Quick Start Guide Step 1: Import the Workflow Copy the workflow JSON Import into your n8n instance The workflow will appear with all nodes connected Step 2: Configure Credentials You'll need to set up the following credentials: OpenAI API**: For AI agents and classification Gmail OAuth2**: For sending emails Google Sheets OAuth2**: For lead logging Google Calendar OAuth2**: For demo scheduling Step 3: Create Your Lead Log Sheet Create a Google Sheet with these columns: Date Name Email Company Job Title Message Number of Employees Industry Geography Annual Revenue Technology Pain Points Lead Classification Step 4: Update Configuration Nodes Replace Sheet ID: Update all Google Sheets nodes with your sheet ID Update Email Templates: Customize all email content Set Escalation Email: Replace "your-email@company.com" with your team's email Configure ICP Criteria: Edit the "Define ICP and Lead Criteria" node 🎯 Lead Classification Setup Define Your ICP (Ideal Customer Profile) Edit the "Define ICP and Lead Criteria" node to set your criteria: 📌 ICP Criteria Example: Company Size: 50+ employees Industry: SaaS, Finance, Healthcare, Manufacturing Geography: North America, Europe Pain Points: Manual processes, compliance needs, scaling challenges Annual Revenue: $5M+ ✅ Demo-Ready Criteria: High-intent prospects who meet multiple qualifying factors: Large company size (your threshold) Clear pain points mentioned Urgent timeline Budget authority indicated Specific solution requests 🌱 Nurture Criteria: Prospects with future potential: Meet basic size requirements In target industry General interest expressed Planning future implementation Exploring options ❌ Drop Criteria: Only drop leads that clearly don't fit: Outside target geography Wrong industry (B2C if you're B2B) Too small with no growth Already with competitor Spam or test messages 📧 Email Customization Customize Follow-Up Sequences: Demo-Ready Sequence: Immediate calendar invitation Personalized demo confirmation Meeting reminder (optional) Nurture Sequence: Welcome email with resources Educational content (Day 2) Webinar/event invitation (Day 3) Demo offer (Day 4) Drop Message: Polite acknowledgment Clear explanation Keep door open for future 🔧 Advanced Configuration AI Answer Agent Setup: Update the system prompt with your company information Add common Q&A patterns Set escalation rules Configure language preferences Lead Enrichment Options: Add API keys for additional data sources Configure enrichment fields Set data quality thresholds Enable duplicate detection Calendar Integration: Set available meeting times Configure meeting duration Add buffer times Set timezone handling 📊 Monitoring and Optimization Track Key Metrics: Lead volume by classification Response rates Demo conversion rates Time to first response Enrichment success rate Optimization Tips: Regular Review: Check classification accuracy weekly A/B Testing: Test different email sequences Feedback Loop: Use outcomes to refine ICP criteria AI Training: Update prompts based on results 🎉 Best Practices Start Simple: Begin with basic criteria and refine over time Test Thoroughly: Use test leads before going live Monitor Daily: Check logs for the first week Iterate Quickly: Adjust based on results Document Changes: Keep track of criteria updates 📈 Scaling Your Workflow As your lead volume grows: Add Sub-workflows: Separate complex processes Implement Queuing: Handle high volumes Add CRM Integration: Sync with your sales tools Enable Analytics: Track detailed metrics Set Up Alerts: Monitor for issues

🚀 Daily IndieHackers Reddit Trend Analysis to Slack > Transform Reddit chaos into actionable startup intelligence > Get AI-powered insights from r/indiehackers delivered to your Slack every morning 🎯 Who's It For This template is designed for startup founders, growth teams, and product managers who need to: Stay ahead of indie hacker trends without manual Reddit browsing Understand what's working in the entrepreneurial community Get actionable insights for product and marketing decisions Keep their team informed about emerging opportunities Perfect for teams building products for entrepreneurs or anyone wanting to leverage community intelligence for competitive advantage. ✨ What It Does Transform your morning routine with automated intelligence gathering that delivers structured, AI-powered summaries of the hottest r/indiehackers discussions directly to your Slack channel. 🧠 Smart Analysis Features | Feature | Description | |---------|-------------| | 🔥 Hotness Scoring | Calculates engagement scores using time-decay algorithms | | 📊 Topic Extraction | Identifies key themes and trending subjects | | 💰 Traction Signals | Spots revenue, metrics, and growth indicators | | 🎯 Theme Clustering | Groups posts into actionable categories | | ⚡ Action Items | Generates specific recommendations for your team | 📱 Slack Integration Receive beautifully formatted messages with: Executive summaries and key takeaways Top 3 hottest posts with engagement metrics Interactive buttons for deeper exploration Team discussion prompts ⚙️ How It Works graph LR A[🕐 Daily 8AM Trigger] --> B[📱 Fetch Reddit Posts] B --> C[🔄 Process Data] C --> D[🤖 Gemini AI Analysis] D --> E[✨ Groq Slack Formatting] E --> F[💬 Deliver to Slack] 🔄 The Complete Process Step 1: Automated Trigger Every morning at 8 AM, the workflow springs into action Step 2: Reddit Data Collection Fetches the latest 5 posts from r/indiehackers with full metadata Step 3: Data Processing Structures raw Reddit data for optimal AI analysis Step 4: AI-Powered Analysis Gemini AI performs deep analysis calculating hotness scores, extracting topics, and identifying patterns Step 5: Slack Formatting Groq AI Agent transforms insights into beautiful Slack Block Kit messages Step 6: Team Delivery Your designated Slack channel receives the formatted analysis 🛠️ Requirements You'll need API access for: Reddit (OAuth2), Google Gemini, Groq, and Slack (OAuth2). All have free tiers available. 🚀 Setup Guide 1️⃣ Configure Your Credentials Add these credentials in n8n: Reddit OAuth2, Google Gemini, Groq, and Slack OAuth2. The workflow will guide you through each setup. 2️⃣ Customize the Schedule Default: Daily at 8:00 AM To modify: Edit the "Daily Schedule" cron trigger node // Example: Run at 9:30 AM { "triggerTimes": { "item": [{ "hour": 9, "minute": 30 }] } } 3️⃣ Set Your Slack Destination Open the "Send to Slack" node Select your target channel Configure notification preferences 4️⃣ Adjust Analysis Parameters Post Limit: Change from default 5 posts // In "Get many posts" Reddit node "limit": 10 // Recommended: 3-10 posts Context Customization: { "channel_type": "team", "audience": "Growth, Product, and Founders", "cta_link": "https://your-dashboard.com", "timeframe_label": "This Week" } 🎨 Customization Options 🔍 Analysis Focus Areas Transform the workflow for different insights: SaaS-Focused Analysis Add to Gemini prompt: "Focus on SaaS and B2B insights, prioritizing recurring revenue and product-market fit signals" Geographic Targeting Add: "Prioritize posts relevant to [your region/market]" Stage-Specific Insights Add: "Focus on [early-stage/growth-stage] startup challenges" 📈 Hotness Algorithm Tweaking Default Formula: (ups + 2*num_comments) * freshness_decay Emphasize Comments: (ups + 3*num_comments) * freshness_decay Include Upvote Ratio: (ups * upvote_ratio + 2*num_comments) * freshness_decay 🌐 Multi-Subreddit Analysis Expand beyond r/indiehackers: Additional Communities: r/startups r/entrepreneur r/SideProject r/buildinpublic r/nocode 💾 Data Storage Extensions Enhance with historical tracking: | Node Type | Purpose | Benefit | |-----------|---------|---------| | Google Sheets | Trend storage | Historical analysis | | Airtable | Advanced data management | Rich analytics | | Webhook | External analytics | Custom dashboards | 📊 Expected Output 📱 Daily Slack Message Structure 🚀 IndieHackers Trends — This Week 📋 TL;DR: [One-sentence key insight] 🔥 Hot Posts (Top 3) [Post Title] (Hotness: 8.7) Topics: SaaS launch, pricing strategy 💬 23 comments | 👍 156 ups | 📅 Posted 4 hours ago [Open Reddit Button] 🧭 Themes Summary Go-to-market tactics — 3 posts, hotness: 24.1 Product launches — 2 posts, hotness: 18.3 ✅ What to Do Now Test pricing page variations based on community feedback Consider cold email strategies mentioned in hot posts Validate product ideas using discussed frameworks [Open Dashboard Button] 💡 Pro Tips for Success 🎯 Optimization Strategies Week 1-2: Baseline Monitor output quality and team engagement Note which insights generate the most discussion Week 3-4: Refinement Adjust AI prompts based on feedback Fine-tune hotness scoring for your needs Month 2+: Advanced Usage Add historical trend analysis Create custom dashboards with stored data Build feedback loops for continuous improvement 🚨 Common Pitfalls to Avoid | Issue | Solution | |-------|---------| | API Rate Limits | Reduce post count or increase time intervals | | Poor Insight Quality | Refine prompts with specific examples | | Team Engagement Drop | Rotate focus areas and encourage thread discussions | | Information Overload | Limit to top 3 posts and key themes only | 🔧 Troubleshooting ❌ Common Issues & Solutions "Model not found" Error Cause: Gemini regional availability Fix: Check supported regions or switch to alternative AI model Slack Formatting Broken Cause: Invalid Block Kit JSON Fix: Validate JSON structure in AI Agent output Missing Reddit Data Cause: API credentials or rate limits Fix: Verify OAuth2 setup and check usage quotas AI Timeouts Cause: Too much data or complex prompts Fix: Reduce post count or simplify analysis requests ⚡ Performance Optimization Keep analysis under 10 posts for optimal speed Monitor execution times in n8n logs Add error handling nodes for production reliability Use webhook timeouts for external API calls 🌟 Advanced Use Cases 📈 Competitive Intelligence Modify prompts to track specific competitors or market segments mentioned in discussions 🎯 Product Validation Focus analysis on posts related to your product category for market research 📝 Content Strategy Use trending topics to inform your content calendar and thought leadership 🤝 Community Engagement Identify opportunities to participate in discussions and build relationships Ready to transform your startup intelligence gathering? 🚀 Deploy this workflow and start receiving actionable insights tomorrow morning!

AI Email Assistant - Smart Email Processing & Response 🤖 A sophisticated n8n workflow that transforms your email management with AI-powered classification, automatic responses, and intelligent organization. 🎯 What This Workflow Does This advanced AI email assistant automatically: Analyzes** incoming emails using intelligent classification Categorizes** messages by priority, urgency, and type Generates** context-aware draft responses in your voice Organizes** emails with smart labeling and filing Alerts** you to urgent messages instantly Manages** attachments with cloud storage integration Perfect for busy professionals, customer service teams, and anyone drowning in email! ✨ Key Features 🧠 Intelligent Email Analysis Context-Aware Processing**: Understands email threads and conversation history Smart Classification**: Automatically categorizes by priority, urgency, and required actions Multi-Criteria Assessment**: Evaluates response needs, follow-up requirements, team involvement Dynamic Label Management**: Syncs with your Gmail labels for consistent organization 📝 AI-Powered Response Generation Professional Draft Creation**: Generates contextually appropriate responses Tone Matching**: Mirrors the formality and style of incoming emails Multiple Response Options**: Provides alternatives for complex inquiries Customizable Voice**: Adapts to your business communication style 🔔 Smart Notification System Urgent Email Alerts**: Instant notifications for high-priority messages Telegram/Slack Integration**: Get alerts where you work Smart Filtering**: Only notifies when truly urgent Quick Action Links**: Direct links to Gmail for immediate response 📎 Advanced Attachment Management Automatic Cloud Upload**: Saves attachments to Google Drive Smart File Naming**: Organized by date, sender, and content Duplicate Detection**: Prevents redundant uploads File Type Filtering**: Optional filtering for security 🏷️ Intelligent Organization Auto-Labeling**: Applies relevant Gmail labels automatically Progress Tracking**: Marks emails as "processed" or "digested" Priority Indicators**: Visual priority levels in your inbox Category-Based Sorting**: Groups similar emails together 🛠️ Setup Instructions Prerequisites n8n instance (cloud or self-hosted) Gmail account with API access OpenAI API key (or compatible AI service) Google Drive account (for attachments) Telegram bot (optional, for alerts) Step 1: Import the Workflow Download AI_Email_Assistant_Community_Template.json In n8n, navigate to Templates → Import from File Select the downloaded JSON file The workflow will import as inactive Step 2: Configure Credentials Gmail Setup: Create Gmail OAuth2 credentials in n8n Configure the following nodes: Email_Trigger Get Conversation Thread Get Latest Message Content Create Draft Response Assign Classification Label Mark as Processed Get All Gmail Labels Test connections to ensure proper authentication AI Model Setup: Configure the AI Language Model node Options include: OpenAI (GPT-4, GPT-3.5-turbo) Anthropic Claude (recommended) Local LLMs via Ollama Add your API credentials Test the connection Google Drive Setup (Optional): Create Google Drive OAuth2 credentials Configure nodes: Upload to Google Drive Check Existing Attachments Replace YOUR_GOOGLE_DRIVE_FOLDER_ID with your folder ID Create a dedicated folder for email attachments Telegram Alerts (Optional): Create a Telegram bot via @BotFather Get your chat ID Configure the Send Urgent Alert node Replace YOUR_TELEGRAM_CHAT_ID with your actual chat ID Step 3: Customize AI Instructions Email Classification (AI Email Classifier node): Review the classification criteria in the system message Adjust urgency keywords for your business Modify priority levels based on your needs Customize category definitions Response Generation (AI Response Generator node): Update the response guidelines Replace [YOUR NAME] with your actual name Adjust tone and style preferences Add company-specific response templates Step 4: Configure Gmail Labels Create Custom Labels in Gmail: High Priority Medium Priority Low Priority Needs Response Urgent Follow Up Required Processed (or use existing labels) Update Label IDs: Run the workflow once to get label IDs Replace YOUR_PROCESSED_LABEL_ID in the "Mark as Processed" node Update any hardcoded label references Step 5: Test and Deploy Testing Process: Send yourself a test email Monitor the workflow execution Verify classification accuracy Check draft response quality Confirm labeling works correctly Test urgent alert functionality Fine-Tuning: Adjust AI prompts based on test results Refine classification criteria Update response templates Modify notification preferences Go Live: Activate the workflow Monitor initial performance Adjust settings as needed 📊 Email Classification System Priority Levels High**: Urgent matters requiring immediate attention Medium**: Important but not time-critical Low**: Routine or informational messages Classification Categories toReply**: Direct questions or requests requiring response urgent**: Immediate business impact or crisis situations dateRelated**: Time-sensitive events or deadlines attachmentsToUpload**: Financial docs or important files requiresFollowUp**: Multi-step processes or ongoing projects forwardToTeam**: Cross-departmental or collaborative items Response Generation Guidelines Professional Tone**: Business casual, warm but professional Context Awareness**: Considers email thread history Structured Responses**: Clear paragraphs with actionable next steps Placeholder System**: Uses [PLACEHOLDER] for missing information Alternative Options**: Provides multiple response choices for complex inquiries 🔧 Advanced Customization File Type Filtering // In Get Specific File Types node, modify: if (mimeType === 'application/pdf' || mimeType === 'text/xml' || mimeType === 'image/jpeg') { // Process file } Custom Urgency Keywords Update the AI classifier prompt with your business-specific urgent terms: Keywords: "URGENT", "EMERGENCY", "CRITICAL", "ASAP", "IMMEDIATE" Custom terms: "CLIENT ESCALATION", "SYSTEM DOWN", "LEGAL DEADLINE" Response Templates Customize the response generator with your company voice: Greeting style: "Hi [Name]" vs "Dear [Name]" Closing: "Best Regards" vs "Thank you" vs "Cheers" Company-specific phrases and terminology Integration Options CRM Systems**: Add nodes to create tasks in your CRM Project Management**: Auto-create tickets in Jira, Asana, etc. Calendar Integration**: Schedule follow-ups automatically Slack/Teams**: Alternative notification channels 🚨 Troubleshooting Common Issues 1. Gmail Authentication Errors Verify OAuth2 credentials are active Check Gmail API quotas Ensure proper scopes are configured 2. AI Classification Inconsistency Review and refine classification prompts Add more specific examples Adjust confidence thresholds 3. Response Generation Problems Validate AI model configuration Check API key and quotas Test with simpler email examples 4. Attachment Upload Failures Verify Google Drive permissions Check folder ID configuration Ensure sufficient storage space 5. Missing Notifications Test Telegram bot configuration Verify chat ID is correct Check urgency classification logic Performance Optimization Rate Limiting**: Gmail has API quotas - monitor usage Batch Processing**: Workflow processes one email at a time Error Handling**: Built-in retry logic for reliability Resource Management**: Monitor AI API costs and usage 📈 Best Practices 1. Email Management Regular Monitoring**: Review classifications weekly Label Hygiene**: Keep Gmail labels organized Feedback Loop**: Manually correct misclassifications Archive Strategy**: Set up auto-archiving for processed emails 2. AI Optimization Prompt Engineering**: Continuously refine AI instructions Example Training**: Add specific examples for your business Context Limits**: Monitor token usage and costs Model Selection**: Choose appropriate AI model for your needs 3. Security Considerations Credential Management**: Regularly rotate API keys Data Privacy**: Review what data is sent to AI services Access Control**: Limit workflow access to authorized users Audit Logging**: Monitor workflow executions 4. Workflow Maintenance Regular Updates**: Keep n8n and node versions current Backup Strategy**: Export workflow configurations regularly Documentation**: Keep setup notes and customizations documented Testing**: Test major changes in development environment first 🤝 Contributing to the Community This workflow template demonstrates: Comprehensive AI Integration**: Multiple AI touchpoints working together Production-Ready Architecture**: Error handling, retry logic, and monitoring Extensive Documentation**: Clear setup and customization guidance Flexible Configuration**: Adaptable to different business needs Best Practice Examples**: Security, performance, and maintenance considerations 📄 License & Support This workflow is provided free to the n8n community under MIT License. Community Resources: n8n Community Forum for questions GitHub Issues for bug reports Documentation updates welcome Professional Support: For enterprise deployments or custom modifications, consider: n8n Cloud for managed hosting Professional services for complex integrations Custom AI model training for specific use cases Transform your email workflow today! 🚀 This AI Email Assistant reduces email processing time by up to 90% while ensuring no important message goes unnoticed. Perfect for busy professionals who want to stay responsive without being overwhelmed by their inbox.

Overview This intelligent chatbot workflow enables natural language conversations with your documents, supporting multiple file formats including PDFs, Word documents, Excel spreadsheets, and text files. Built with advanced RAG (Retrieval-Augmented Generation) technology, this chatbot can understand, analyze, and answer questions about your document content with contextual accuracy and intelligent responses. How It Works Intelligent Document Processing & Conversation Pipeline: Multi-Format Document Ingestion**: Automatically processes and indexes various document formats (PDF, DOCX, XLSX, TXT, etc.) Smart Content Chunking**: Breaks down documents into meaningful segments while preserving context and relationships Vector Database Storage**: Creates searchable embeddings for fast and accurate information retrieval Contextual Conversation Engine**: Uses AI to understand user queries and retrieve relevant document sections Natural Language Responses**: Generates human-like responses with citations and source references Multi-Turn Conversations**: Maintains conversation history and context across multiple interactions Real-Time Processing**: Instant responses with live document updates and dynamic content refresh Setup Instructions Estimated Setup Time: 15-20 minutes Prerequisites n8n instance (v0.200.0 or higher recommended) OpenAI/Gemini API key for embeddings and chat completion Vector database service (optional: Pinecone, Weaviate, or Qdrant) File storage service (optional: Google Drive, Dropbox, AWS S3) Web server for chatbot interface (optional) Configuration Steps Configure Document Input Sources Set up file upload webhook for direct document submission Configure cloud storage watchers for automatic document processing Add support for multiple file formats and size limits Set up document validation and security checks Setup Document Processing Pipeline Configure text extraction engines for different file types Set up intelligent chunking parameters (chunk size, overlap, boundaries) Add metadata extraction for document categorization Configure OCR for scanned documents (optional) Configure Vector Database Set up your chosen vector database credentials Configure embedding model settings (Gemini models/text-embedding-004 recommended) Set up collection/index structure for document storage Configure search parameters and similarity thresholds Setup AI Chat Engine Add your AI service API credentials (Gemini, Claude, etc.) Configure conversation prompts and system instructions Set up context window management and token optimization Add response formatting and citation rules Configure Chat Interface Set up webhook endpoints for chat API Configure session management and conversation history Add authentication and rate limiting (optional) Set up real-time updates and streaming responses Setup Monitoring & Analytics Configure conversation logging and analytics Set up performance monitoring for response times Add usage tracking and cost monitoring Configure error handling and failover mechanisms Use Cases Business & Enterprise Knowledge Base Queries**: Ask questions about company policies, procedures, and documentation Contract Analysis**: Query legal documents, contracts, and compliance materials Training Materials**: Interactive learning with training manuals and educational content Financial Reports**: Analyze and discuss financial statements, budgets, and forecasts Research & Academia Research Paper Analysis**: Discuss findings, methodologies, and citations from academic papers Literature Reviews**: Compare and contrast multiple research documents Thesis Support**: Get insights from reference materials and research data Grant Proposals**: Analyze requirements and optimize proposal content Legal & Compliance Legal Document Review**: Query contracts, agreements, and legal texts Regulatory Compliance**: Understand compliance requirements from regulatory documents Case Law Research**: Analyze legal precedents and court decisions Policy Analysis**: Interpret organizational policies and procedures Technical Documentation API Documentation**: Interactive queries about technical specifications User Manuals**: Get help and guidance from product documentation Code Documentation**: Understand codebases and technical implementations Troubleshooting Guides**: Interactive problem-solving with technical guides Personal Productivity Document Summarization**: Get quick summaries of long documents Information Extraction**: Find specific data points across multiple documents Content Research**: Research topics across your personal document library Meeting Notes**: Query and analyze meeting transcripts and notes Key Features Advanced Document Processing Multi-Format Support**: PDF, DOCX, XLSX, TXT, PPTX, and more Intelligent Chunking**: Context-aware document segmentation Metadata Extraction**: Automatic categorization and tagging OCR Integration**: Process scanned documents and images with text Intelligent Conversation Contextual Understanding**: Maintains conversation context and document relationships Source Attribution**: Provides citations and references for all answers Multi-Document Queries**: Compare and analyze across multiple documents Follow-up Questions**: Natural conversation flow with clarifying questions Performance & Scalability Fast Retrieval**: Vector-based semantic search for instant responses Scalable Architecture**: Handle large document collections efficiently Batch Processing**: Process multiple documents simultaneously Caching System**: Optimized response times with intelligent caching Security & Privacy Document Encryption**: Secure storage and transmission of sensitive documents Access Control**: User-based permissions and document access restrictions Audit Logging**: Complete conversation and access audit trails Data Retention**: Configurable data retention and deletion policies Technical Architecture Document Processing Flow File Upload → Format Detection → Text Extraction → Content Chunking Metadata Extraction → Embedding Generation → Vector Storage → Index Creation Conversation Flow User Query → Intent Analysis → Vector Search → Context Retrieval Response Generation → Source Attribution → Answer Formatting → Delivery Supported File Formats Documents**: PDF, DOC, DOCX, RTF, TXT, MD Spreadsheets**: XLS, XLSX, CSV Presentations**: PPT, PPTX Images**: PNG, JPG (with OCR) Archives**: ZIP (auto-extracts supported formats) Web**: HTML, XML Integration Options Chat Interfaces Web Widget**: Embeddable chat widget for websites API Endpoints**: RESTful API for custom integrations Slack/Teams**: Direct integration with team collaboration tools Mobile Apps**: API-first design for mobile application integration Data Sources Cloud Storage**: Google Drive, Dropbox, OneDrive, AWS S3 Document Systems**: SharePoint, Confluence, Notion Email**: Process attachments from email systems CRM/ERP**: Integration with business systems Performance Specifications Response Time**: < 3 seconds for typical queries Document Capacity**: Supports collections of 10,000+ documents Concurrent Users**: Scales to handle multiple simultaneous conversations Accuracy**: >90% relevance for domain-specific queries Advanced Configuration Options Customization Custom Prompts**: Tailor AI behavior for specific use cases Branding**: Customize chat interface with your company branding Language Support**: Multi-language document processing and responses Domain Expertise**: Fine-tune for specific industries or domains Analytics & Monitoring Usage Analytics**: Track popular queries and document usage Performance Metrics**: Monitor response times and accuracy User Feedback**: Collect ratings and improve responses A/B Testing**: Test different configurations and prompts Troubleshooting & Support Common Issues Slow Responses**: Check vector database performance and API limits Inaccurate Answers**: Review chunking strategy and embedding quality Format Errors**: Verify document formats and processing capabilities Memory Issues**: Monitor token usage and context window limits Optimization Tips Use clear, specific questions for best results Ensure documents are well-formatted with proper headers Regular vector database maintenance for optimal performance Monitor API usage to optimize costs and performance

AI-Powered Academic Assignment Generator This n8n workflow template automates the complete academic assignment generation process from student queries to professional document delivery. Students submit assignment requests via Telegram, and the workflow generates comprehensive, plagiarism-free academic content using Google Gemini AI, formats it into professional PDF documents, and delivers downloadable links while maintaining complete records. What does this workflow do? 📱 Telegram Integration**: Receives structured assignment requests from students 🤖 AI Content Generation**: Creates comprehensive academic answers (500+ words per question) 📄 Professional Formatting**: Generates university-standard HTML/PDF documents ☁️ Cloud Storage**: Automatically stores files in organized Google Drive folders 📊 Record Keeping**: Maintains complete assignment database in Google Sheets 🔄 End-to-End Automation**: Complete pipeline from query to document delivery How it works The workflow processes student assignment requests through 16 interconnected nodes, handling everything from input parsing to final document delivery: Input → AI Processing → Document Generation → Storage & Delivery Setup Requirements Credentials needed: Telegram Bot Token** (for receiving/sending messages) Google Gemini API Key** (for AI content generation) Google Sheets API** (for record keeping) Google Drive API** (for file storage) PDFCrowd API** (for PDF conversion) Pre-setup steps: Create a Telegram bot and obtain the bot token Set up Google Drive folder structure for file organization Create Google Sheets template with proper column headers Configure API rate limits and usage quotas Workflow Breakdown 🔌 Input Processing Nodes Student Query Intake Bot (Telegram Trigger) Student Query Intake Bot (Telegram Trigger) Listens for incoming student messages with assignment details Monitors specific chat ID for authorized users Triggers workflow when structured assignment requests are received Structured Data Parser (Code Node) Extracts student information using regex patterns Parses: Name, Faculty, Department, Level, Course, Registration Number Automatically sets current date and handles missing data Outputs clean JSON structure for AI processing 🤖 AI Processing Nodes Student Assignment Auto-Composer (LangChain Agent) Main AI orchestrator for assignment generation Uses structured prompts for consistent academic formatting Generates 500-word answers per question with APA citations Ensures plagiarism-free, original academic content Generator Model (Google Gemini Chat) Primary AI model for high-quality content generation Handles complex academic writing and formatting requirements Fallback Model Generator (Google Gemini - Gemma) Backup AI model ensuring workflow reliability Activates when primary model encounters issues Structured Output Parser (LangChain) Validates AI-generated content against JSON schema Enforces required field compliance and format consistency Auto-fixes common formatting issues 🔧 Processing & Error Handling Error Handler (Code Node) Handles text processing errors and data type issues Converts non-string values and provides error recovery Ensures workflow continuity even with problematic data Wait Node Introduces strategic 2-second delay for processing stability Allows AI processing to complete before next steps 📊 Data Management Nodes Edit Fields (Set Node) Maps AI output to Google Sheets column structure Ensures data consistency for database storage Long Essay Record Sheet (Google Sheets) Stores complete assignment records with metadata Maintains comprehensive student assignment database Uses Name field as unique identifier for record updates 📄 Document Generation Nodes Static HTML Builder (LangChain Agent) Converts structured data into professional HTML documents Applies academic formatting: Times New Roman, 12pt, double-spaced Creates university-standard document structure HTTP Request (PDF Conversion) Converts HTML to high-quality PDF using PDFCrowd API Maintains academic formatting and professional appearance Uses student name for file identification ☁️ Storage & Delivery Nodes Upload File (Google Drive) Stores generated PDFs in organized Drive folders Creates shareable links for easy access Maintains systematic file organization Send Text Message (Telegram) Delivers Google Drive download link to student Completes the automation cycle with instant access Input Format Students should format their Telegram messages as follows: Name: John Doe Faculty: Engineering Department: Computer Science Level: 200L Course: CSC 201 - Data Structures Reg number: 2024001234 Question: Explain the concept of Big O notation Compare different sorting algorithms Discuss the applications of binary trees Features ✨ Intelligent Processing Smart Input Parsing**: Handles unstructured text inputs automatically Multi-Question Support**: Processes complex assignment requirements Data Validation**: Ensures complete and accurate information capture 🎓 Academic Excellence University Standards**: Professional formatting and citation styles Original Content**: Plagiarism-free AI-generated assignments Comprehensive Answers**: 500+ words per question with detailed explanations 🛡️ Reliability & Error Handling Fallback Systems**: Multiple AI models for continuous operation Error Recovery**: Automatic handling of processing issues Data Integrity**: Schema validation and field verification Use Cases This workflow template is perfect for: 📚 Educational Institutions**: Automate student assignment processing and grading assistance 👨‍🎓 Academic Support Services**: Provide structured learning assistance and content generation 🏫 Online Learning Platforms**: Integrate assignment automation into educational systems 📝 Content Creation Services**: Generate academic-quality content for educational purposes 🤖 AI Learning Projects**: Implement complex AI workflows with multiple service integrations Output Examples Generated Assignment Features: Professional formatting** with Times New Roman, 12pt font, double-spacing Complete academic structure** including headers, student information, questions, and references Comprehensive answers** averaging 500+ words per question with detailed explanations Proper citations** in APA format with authentic academic references PDF delivery** through shareable Google Drive links Database Records: Complete student information tracking Assignment question and answer storage Timestamp and metadata preservation Easy retrieval and analysis capabilities Performance & Reliability Processing Time: 2-3 minutes per assignment Success Rate: >95% with fallback mechanisms Content Quality: University-standard academic writing Scalability: Handles multiple concurrent requests Error Recovery: Automatic retry and alternative processing paths Customization Options Easily configurable elements: Chat IDs**: Modify for different Telegram groups or users AI Models**: Switch between different Google Gemini models Document Formatting**: Adjust academic standards and styling Storage Locations**: Configure Google Drive folders and naming conventions Database Fields**: Modify Google Sheets columns and data structure Advanced customizations: Add support for different document formats (Word, LaTeX) Integrate additional AI providers (OpenAI, Claude, etc.) Implement grading and feedback mechanisms Add multi-language support Create batch processing capabilities Getting Started Import the workflow into your n8n instance Configure credentials for all required services Set up Telegram bot and obtain necessary permissions Create Google Drive folders and Google Sheets template AI-Powered Academic Assignment Test with sample data to ensure proper functionality Deploy and monitor for production use Tags academic education ai telegram google-sheets pdf-generation automation langchain assignment student-support

Overview Automates your complete social media content pipeline: sources articles from Wallabag RSS, generates platform-specific posts with AI, creates contextual images, and publishes via GetLate API. Built with 63 nodes across two workflows to handle LinkedIn, Instagram, and Bluesky—with easy expansion to more platforms. Ideal for: Content marketers, solo creators, agencies, and community managers maintaining a consistent multi-platform presence with minimal manual effort. How It Works Two-Workflow Architecture: Content Aggregation Workflow Monitors Wallabag RSS feeds for tagged articles (#to-share-linkedin, #to-share-instagram, etc.) Extracts and converts content from HTML to Markdown Stores structured data in Airtable with platform assignment AI Generation & Publishing Workflow Scheduled trigger queries Airtable for unpublished content Routes to platform-specific sub-workflows (LinkedIn, Instagram, Bluesky) LLM generates optimized post text and image prompts based on custom brand parameters Optionally generates AI images and hosts them on Imgbb CDN Publishes via GetLate API (immediate or draft mode) Updates Airtable with publication status and metadata Key Features: Tag-based content routing using Wallabag's native system Swappable AI providers (Groq, OpenAI, Anthropic) Platform-specific optimization (tone, length, hashtags, CTAs) Modular design—duplicate sub-workflows to add new platforms in \~30 minutes Centralized Airtable tracking with 17 data points per post Set Up Steps Setup time: \~45-60 minutes for initial configuration Create accounts and get API keys (\~15 min) Wallabag (with RSS feeds enabled) GetLate (social media publishing) Airtable (create base with provided schema—see sticky notes) LLM provider (Groq, OpenAI, or Anthropic) Image service (Hugging Face, Fal.ai, or Stability AI) Imgbb (image hosting) Configure n8n credentials (\~10 min) Add all API keys in n8n's credential manager Detailed credential setup instructions in workflow sticky notes Set up Airtable database (\~10 min) Create "RSS Feed - Content Store" base Add 19 required fields (schema provided in workflow sticky notes) Get Airtable base ID and API key Customize brand prompts (\~15 min) Edit "Set Custom SMCG Prompt" node for each platform Define brand voice, tone, goals, audience, and image preferences Platform-specific examples provided in sticky notes Configure platform settings (\~10 min) Set GetLate account IDs for each platform Enable/disable image generation per platform Choose immediate publish vs. draft mode Adjust schedule trigger frequency Test and deploy Tag test articles in Wallabag Monitor the first few executions in draft mode Activate workflows when satisfied with the output Important: This is a proof-of-concept template. Test thoroughly with draft mode before production use. Detailed setup instructions, troubleshooting tips, and customization guidance are in the workflow's sticky notes. Technical Details 63 nodes**: 9 Airtable operations, 8 HTTP requests, 7 code nodes, 3 LangChain LLM chains, 3 RSS triggers, 3 GetLate publishers Supports**: Multiple LLM providers, multiple image generation services, unlimited platforms via modular architecture Tracking**: 17 metadata fields per post, including publish status, applied parameters, character counts, hashtags, image URLs Prerequisites n8n instance (self-hosted or cloud) Accounts: Wallabag, GetLate, Airtable, LLM provider, image generation service, Imgbb Basic understanding of n8n workflows and credential configuration Time to customize prompts for your brand voice Detailed documentation, Airtable schema, prompt examples, and troubleshooting guides are in the workflow's sticky notes. Category Tags #social-media-automation, #ai-content-generation, #rss-to-social, #multi-platform-posting, #getlate-api, #airtable-database, #langchain, #workflow-automation, #content-marketing

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.

🚀 Automate your social media presence! This workflow duo automatically curates content from your Wallabag RSS feeds, generates platform-specific posts using AI, and publishes them—complete with AI-generated images. ⚙️ Setup & Configuration Required Credentials & Services: To run this workflow, you will need to set up the following credentials in n8n: Wallabag RSS Feeds:** This workflow uses Wallabag as your content curation service. Wallabag is the most cost-effective option—easy to self-host or use as a paid service. You'll need to generate access tokens for RSS feed access. Airtable API Key:** To create and update records in your "Content Store" database. LLM Provider API Key:* To power the social media content generation. The demo uses *Groq (llama-3.3-70b-versatile)**, but this can be replaced with any preferred LLM (OpenAI, Anthropic, etc.). GetLate API Key:** To authenticate and post to your social media platforms. Imgbb API Key:** To host the AI-generated images. An Image Generation Service API Key:* For creating images from prompts. The demo uses *Hugging Face (stable-diffusion-xl-base-1.0)**, but this can be swapped for any other service (Fal.ai, Stability AI, etc.). Key Setup Requirement: Define Your Tagging Convention Before running this workflow, establish a consistent tagging system in Wallabag where each tag corresponds to a specific social media platform. For example: #to-share-linkedin for LinkedIn content #to-share-bluesky for Bluesky content #to-share-instagram for Instagram content Adding More Feeds & Platforms: New Feeds:** Simply duplicate the example sub-workflows and update the RSS feed URL and target platform information for each new tag/platform combination. New Platforms:** To add support for additional social platforms, duplicate one of the existing platform sub-workflows (LinkedIn or Bluesky) and update the platform-specific parameters, prompts, and GetLate API settings for your new platform. Airtable Database Schema: "RSS Feed - Content Store" The workflow uses an Airtable base with the following fields to track content from ingestion to publication: id (Primary Field): Formula, for unique record ID (RECORD_ID()) audience_targeted: Long text author: Long text character_count: Number content_markdown: Long text cta_used: Long text feed_id: Single line text goal_applied: Long text image_filename: Singe line text image_id: Single line text image_link: URL image_prompt: Long text is_posted: Number, default is 0 platform: Single line text post_text: Long text suggested_hashtags: Long text title: Long text tone_applied: Long text article_url: URL Customizable User Inputs: This workflow is built for flexibility. Key inputs you can customize include: Wallabag RSS Feed URL & Platform Tag:** The specific feed and platform-specific tag (e.g., #to-share-linkedin) to monitor in Workflow #1. Target Social Platform:** Defined per feed in the "Edit Field" node. Content Generation Schedule:** The frequency for auto-posting in the Schedule Trigger. Brand Voice & LLM Parameters:** The tone, style, and specific instructions for the AI in the "Set Custom SMCG Prompt" node. Platform-Specific Prompts:** The template used to generate posts for each social network (Instagram, LinkedIn, etc.). Posting Behavior & Image Generation:* Configured within the *SMCG (Social Media Content Generation) node**. This is where you set the posting mode (immediate vs. draft) and define a boolean for each platform to enable or disable AI-generated images for its posts. 📥 Workflow 1: RSS Aggregator & Content Store RSS Trigger** → Pulls tagged articles from Wallabag feeds using platform-specific tags Platform Assignment** → Sets target social platform based on tag Content Conversion** → HTML to Markdown formatting Airtable Storage** → Saves articles to content database 🌟 Adding New RSS Feeds: To monitor additional Wallabag feeds for different content sources, simply duplicate the existing RSS feed sub-workflow and update the RSS URL with your new Wallabag access token and platform-specific tag. Each feed can target a different social platform or content category. 🔄 Workflow 2: AI Content Generator & Publisher Schedule Trigger** → Runs on your preferred frequency Content Selection** → Pulls unpublished articles from Airtable AI Configuration** → Sets brand voice, posting behavior, and image generation preferences Platform Routing** → Directs to appropriate social platform workflow AI Content Generation** → Creates posts and image prompts using LLM Image Generation** → Creates & hosts images when enabled Social Publishing** → Posts to platforms via GetLate API Database Update** → Marks content as published in Airtable 🌟 Adding New Platform Support: To extend this workflow to additional social platforms, simply duplicate one of the existing platform sub-workflows and update the platform-specific parameters, LLM prompts, and GetLate API configuration for your target platform.

Automated solution to extract and organize contact information from Upwork job postings, enabling direct outreach to potential clients who post jobs matching your expertise. 🚀 What It Does Scrapes job postings for contact information Extracts email addresses and social profiles Organizes leads in a structured format Enables direct outreach campaigns Tracks response rates 🎯 Perfect For Freelancers looking to expand their client base Agencies targeting specific industries Sales professionals in the gig economy Recruiters sourcing clients Digital marketing agencies ⚙️ Key Benefits ✅ Access to hidden contact information ✅ Expand your client base ✅ Beat the competition to opportunities ✅ Targeted outreach campaigns ✅ Higher response rates 🔧 What You Need Upwork account n8n instance Email service (for outreach) CRM (optional) 📊 Features Email pattern detection Social media profile extraction Company website discovery Lead scoring system Outreach tracking 🛠️ Setup & Support Quick Setup Start collecting leads in 20 minutes with our step-by-step guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Take control of your freelance career with direct access to potential clients. Transform how you find and secure projects on Upwork.

This workflow uses AI to analyze the content of every new message in Gmail and then assigns specific labels, according to the context of the email. Default configuration of the workflow includes 3 labels: „Partnership” - email about sponsored content or cooperation, „Inquiry” - email about products, services, „Notification” - email that doesn't require response. You can add or edit labels and descriptions according to your use case. 🎬 See this workflow in action in my YouTube video about automating Gmail. How it works? Gmail trigger performs polling every minute for new messages (you can change the trigger interval according to your needs). The email content is then downloaded and forwarded to an AI chain. 💡 The prompt in the AI chain node includes instructions for applying labels according to the email content - change label names and instructions to fit your use case. Next, the workflow retrieves all labels from the Gmail account and compares them with the label names returned from the AI chain. Label IDs are aggregated and applied to processed email messages. ⚠️ Label names in the Gmail account and workflow (prompt, JSON schema) must be the same. Set up steps Set credentials for Gmail and OpenAI. Add labels to your Gmail account (e.g. „Partnership”, „Inquiry” and „Notification”). Change prompt in AI chain node (update list of label names and instructions). Change list of available labels in JSON schema in parser node. Optionally: change polling interval in Gmail trigger (by default interval is 1 minute). If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

This n8n workflow is designed to work on the local network and assists with reconciling downloaded bank statements with internal tenant records to quickly highlight any issues with payments such as missed or late payments or those of incorrect amounts. This assistant can then generate a report to quick flag attention to ensure remedial action is taken. How it works The workflow monitors a local network drive to watch for new bank statements that are added. This bank statement is then imported into the n8n workflow, its contents extracted and sent to the AI Agent. The AI Agent analyses the line items to identify the dates and any incoming payments from tenants. The AI agent then uses an locally-hosted Excel ("XLSX") spreadsheet to get both tenant records and property records. From this data, it can determine for each active tenant when payment is due, the amount and the tenancy duration. Comparing to the bank statement, the AI Agent can now report on where tenants have missed their payments, made late payments or are paying the incorrect amounts. The final report is generated and logged in the same XLSX for a human to check and action. Requirements A self-hosted version of n8n is required. OpenAI account for the AI model Customising this workflow If you organisation has a Slack or Teams account, consider sending reports to a channel for increased productivity. Email may be a good choice too. Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/1YRKjfakpInm23F_g8AHupKPBN-fphWgK/view?usp=sharing

This n8n template demonstrates how to calculate the evaluation metric "Correctness" which in this scenario, measures the compares and classifies the agent's response against a set of ground truths. The scoring approach is adapted from the open-source evaluations project RAGAS and you can see the source here https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py How it works This evaluation works best where the agent's response is allowed to be more verbose and conversational. For our scoring, we classify the agent's response into 3 buckets: True Positive (in answer and ground truth), False Positive (in answer but not ground truth) and False Negative (not in answer but in ground truth). We also calculate an average similarity score on the agent's response against all ground truths. The classification and the similarity score is then averaged to give the final score. A high score indicates the agent is accurate whereas a low score could indicate the agent has incorrect training data or is not providing a comprehensive enough answer. Requirements n8n version 1.94+ Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing