Workflow Templates

Demo Personalized Email This n8n workflow is built for AI and automation agencies to promote their workflows through an interactive demo that prospects can try themselves. The featured system is a deep personalized email demo. 🔄 How It Works Prospect Interaction A prospect starts the demo via Telegram. The Telegram bot (created with BotFather) connects directly to your n8n instance. Demo Guidance The RAG agent and instructor guide the user step-by-step through the demo. Instructions and responses are dynamically generated based on user input. Workflow Execution When the user triggers an action (e.g., testing the email demo), n8n runs the workflow. The workflow collects website data using Crawl4AI or standard HTTP requests. Email Demo The system personalizes and sends a demo email through SparkPost, showing the automation’s capability. Logging and Control Each user interaction is logged in your database using their name and id. The workflow checks limits to prevent misuse or spam. Error Handling If a low-CPU scraping method fails, the workflow automatically escalates to a higher-CPU method. ⚙️ Requirements Before setting up, make sure you have the following: n8n — Automation platform to run the workflow Docker — Required to run Crawl4AI Crawl4AI — For intelligent website crawling Telegram Account — To create your Telegram bot via BotFather SparkPost Account — To send personalized demo emails A database (e.g., PostgreSQL, MySQL, or SQLite) — To store log data such as user name and ID 🚀 Features Telegram interface** using the BotFather API Instructor and RAG agent** to guide prospects through the demo Flow generation limits per user ID** to prevent abuse Low-cost yet powerful web scraping**, escalating from low- to high-CPU flows if earlier ones fail 💡 Development Ideas Replace the RAG logic with your own query-answering and guidance method Remove the flow limit if you’re confident the demo can’t be misused Swap the personalized email demo with any other workflow you want to showcase 🧠 Technical Notes Telegram bot** created with BotFather Website crawl process:** Extract sub-links via /sitemap.xml, sitemap_index.xml, or standard HTTP requests Fall back to Crawl4AI if normal requests fail Fetch sub-link content via HTTPS or Crawl4AI as backup SparkPost** used for sending demo emails ⚙️ Setup Instructions 1. Create a Telegram Bot Use BotFather on Telegram to create your bot and get the API token. This token will be used to connect your n8n workflow to Telegram. 2. Create a Log Data Table In your database, create a table to store user logs. The table must include at least the following columns: name — to store the user’s name or Telegram username. id — to store the user’s unique identifier. 3. Install Crawl4AI with Docker Follow the installation guide from the official repository: 👉 https://github.com/unclecode/crawl4ai Crawl4AI** will handle website crawling and content extraction in your workflow. 📦 Notes This setup is optimized for low cost, easy scalability, and real-time interaction with prospects. You can customize each component — Telegram bot behavior, RAG logic, scraping strategy, and email workflow — to fit your agency’s demo needs. 👉 You can try the live demo here: @email_demo_bot

CV → Match → Screen → Decide, all automated This workflow automatically processes candidate CVs from email, intelligently matches them to job descriptions, performs AI-powered screening analysis, and sends actionable summaries to your team in Slack. Good to know Handles both PDF and Word document CVs automatically Two-stage JD matching: prioritizes role mentioned in candidate's email, falls back to CV analysis if needed Uses Google Gemini API for AI screening (generous free tier and rate limits, typically enough to avoid paying for API requests, but check latest pricing at Google AI Pricing) All CVs stored in Google Drive with standardized naming (candidate name + date/time) Complete audit trail logged in Google Sheets Who's it for Hiring teams and recruiters who want to automate first-round CV screening while maintaining quality. Perfect for companies receiving high volumes of applications across multiple roles, especially in tech, sales, or automation-focused positions. How it works Gmail monitors inbox for CVs with specific label and downloads attachments Detects file type (PDF or Word) and converts/standardizes format for text extraction AI agent matches candidate to best-fit job description by analyzing email context first (if candidate mentioned a role), or CV content as fallback (selects up to 3 potential JD matches) If multiple JDs matched, second AI agent selects the single best fit AI recruiter agent analyzes CV against selected JD and generates structured screening report (strengths, weaknesses, risk/reward factors, overall fit score 0-10 with justification) Extracts candidate details (name, email) from CV text Logs complete analysis to Google Sheets tracker Sends formatted summary to Slack with Proceed/Reject action buttons for instant team decisions Requirements Gmail account with API access Google Drive account (OAuth2) Google Sheets account (OAuth2) Slack workspace with bot permissions Google Gemini API key (Get free key here) Google Drive folders: one for CVs, one for Job Descriptions (as PDFs or Google Docs) How to set up Add credentials: Gmail OAuth2, Google Drive OAuth2, Google Sheets OAuth2, Slack OAuth2, Google Gemini API Create Gmail label (e.g., "CV-Screening") for incoming candidate emails In "Receive CV via Email" node: select your Gmail label for filtering Create two Google Drive folders: "Candidate CVs" and "Job Descriptions" In "Upload CV - PDF" and "Stream Doc/Docx File" nodes: update folder ID to your "Candidate CVs" folder In "Access JD Files" node: update folder ID to your "Job Descriptions" folder Create Google Sheet named "AI Candidate Screening" with columns matching the sample AI Candidate Screening sheet In "Append row in sheet" node: select your Google Sheet In "Send Candidate Screening Confirmation" node: select your Slack channel Activate workflow Customizing this workflow Change JD matching logic: Edit "JD Matching Agent" node prompt to adjust how CVs are matched to roles (e.g., weight technical skills vs. experience). Change "Company Description" in AI prompts: Insert your "Company Description" in System Message sections in "JD Matching Agent" and "Detailed JD Matching Agent" nodes Modify screening criteria: Edit "Recruiter Scoring Agent" node system message to focus on specific qualities (culture fit, leadership, technical depth, etc.) Add more storage locations: Add nodes to save CVs to other systems (Notion, Airtable, ATS platforms) Customize Slack message: Edit "Send Candidate Screening Confirmation" node to change formatting, add more context, or include additional candidate data Auto-proceed logic: Add IF node after screening to auto-proceed candidates with fit score above threshold (e.g., 8+/10) Add email responses: Connect nodes to automatically email candidates (confirmation, rejection, interview invite) Add human-in-the-loop: Sub-workflow triggered by Slack response or email response, to update Sheet with approve/reject status Add candidate email responses + interview scheduling**: For approved candidates, trigger email to candidate with Cal.com or Calendly link so they can book their interview Quick Troubleshooting No CVs being processed: Check Gmail label is correctly set in "Receive CV via Email" node and emails are being labeled Word documents failing: Verify "Stream Doc/Docx File" node has correct parent folder ID and Google Drive credentials authorized JD matching returns no results: Check "Access JD Files" node folder ID points to your Job Descriptions folder, and JD files are named clearly (e.g., "Marketing Director JD.pdf") JD matching is not relevant for my company: Update the "Company Description" in the System Messages in the "JD Matching Agent" and "Detailed JD Matching Agent" nodes "Can't find matching JD": Ensure candidate's email mentions role name OR their CV clearly indicates relevant experience for available JDs Google Sheets errors: Verify sheet name is "AI Candidate Screening" and column headers exactly match workflow expectations (Submission ID, Date, CV, First Name, etc.) Slack message not appearing: Re-authorize Slack credentials and confirm channel ID in "Send Candidate Screening Confirmation" node Missing candidate name/email: CV text must be readable - check PDF extraction quality or try converting complex CVs to simpler format 401/403 API errors: Re-authorize all OAuth2 credentials (Gmail, Google Drive, Google Sheets, Slack) AI analysis quality issues: Edit system prompts in "JD Matching Agent" and "Recruiter Scoring Agent" nodes to refine screening criteria Sample Outputs Google Sheets - AI Candidate Screening - sample Slack confirmation message Acknowledgments This workflow was inspired by Nate Herk's YouTube demonstration on building a resume analysis system. This implementation builds upon that foundation by adding dynamic job description matching (initial + detailed JD matching agents), Slack Block Kit integration with interactive buttons, updated Google Drive API methods for document handling, and enhanced candidate data capture in Google Sheets.

This n8n template transforms your daily meeting preparation by automatically researching attendees and generating comprehensive briefing documents. Every weekday morning, it analyzes your calendar events, researches each external attendee using multiple data sources, and delivers professionally formatted meeting briefs directly to your Slack channel. Who's it for Business professionals, sales teams, account managers, and executives who regularly attend meetings with external contacts and want to arrive fully prepared with relevant context, conversation starters, and strategic insights about their attendees. How it works The workflow triggers automatically Monday through Friday at 6 AM, fetching your day's calendar events and filtering for meetings with external attendees. For each meeting, an AI agent researches attendees using your CRM (Attio), email history (Gmail), past calendar interactions, and external company research via Perplexity when needed. The system then generates structured meeting briefs containing attendee background, relationship context, key talking points, and strategic objectives, delivering everything as a formatted Slack message to start your day. Requirements Google Calendar with OAuth2 credentials Gmail with OAuth2 credentials Slack workspace with bot token and channel access Attio CRM with API bearer token OpenRouter API key for AI model access (or other API credentials to connect AI to your AI agents) Perplexity API key for company research How to set up Configure credentials for all required services in your n8n instance Update personal identifiers in the workflow: Replace "YOUR_EMAIL@example.com" with your actual calendar email in both Google Calendar nodes Replace "YOUR_SLACK_CHANNEL_ID" with your target channel ID in both Slack nodes Adjust AI models in OpenRouter nodes based on your preferences and model availability Test the workflow manually with a day that contains external meetings Verify Slack formatting appears correctly in your channel How to customize the workflow Change meeting research depth: Modify the AI agent prompt to focus on specific research areas like company financial data, recent news, or technical background. Adjust notification timing: Update the cron expression in the Schedule Trigger to run at different times or days. Expand CRM integration: Add additional Attio API calls to capture more contact details or create follow-up tasks. Enhance Slack formatting: Customize the Block Kit message structure in the JavaScript code node to include additional meeting metadata or visual elements. Add more research sources: Connect additional tools like LinkedIn, company databases, or news APIs to the AI agent for richer attendee insights. The template uses multiple AI models through OpenRouter for different processing stages, allowing you to optimize costs and performance by selecting appropriate models for research tasks versus text formatting operations.

Who’s it for Marketing, growth, and analytics teams who want a decision-ready GA4 summary—automatically calculated, clearly color-coded, and emailed as a polished HTML report. How it works / What it does Get Client (Form Trigger)* collects *GA4 Property ID (“Account ID”), **Key Event, date ranges (current & previous), Client Name, and recipient email. Overall Metrics This Period / Previous Period (GA4 Data API)** pull sessions, users, engagement, bounce rate, and more for each range. Form Submits This Period / Previous Period (GA4 Data API)** fetch key-event counts for conversion comparisons. Code** normalizes form dates for API requests. AI Agent* builds a *valid HTML email**: Calculates % deltas, applies green for positive (#10B981) and red for negative (#EF4444) changes. Writes summary and recommendations. Produces the final HTML only. Send a message (Gmail)** sends the formatted HTML report to the specified email address with a contextual subject. How to set up 1) Add credentials: Google Analytics OAuth2, OpenAI (Chat), Gmail OAuth2. 2) Ensure the form fields match your GA4 property and event names; “Account ID” = GA4 Property ID. Property ID - https://take.ms/vO2MG Key event - https://take.ms/hxwQi 3) Publish the form URL and run a test submission. Requirements GA4 property access (Viewer/Analyst) • OpenAI API key • Gmail account with send permission. Resources Google OAuth2 (GA4) – https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/ OpenAI credentials – https://docs.n8n.io/integrations/builtin/credentials/openai/ Gmail OAuth2 – https://docs.n8n.io/integrations/builtin/credentials/google/ GA4 Data API overview – https://developers.google.com/analytics/devguides/reporting/data/v1

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.

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 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

How it works: Whenever a new event is scheduled on your Google Calendar, this workflow generates a Meeting Briefing email, giving an overview of each person on the call and the company they work for. It makes use of the web search tool on the OpenAI Responses API to make lookups. The workflow triggers when a new event is added to the calendar, loops over each attendee, generating reports on each person and their company, collates the results, and sends the briefing as an email. Set up steps: Add your credentials for Google Calendar (for viewing events) and Gmail (to send the email) Add your OpenAI credentials as a Header Auth on the Company Search and Person Search nodes. Name: Authorization Value: Bearer {{ YOUR_API_KEY }} Edit the "Edit Fields" node with the email that you want to send the briefing to, and a short bit of context about yourself.

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

🎯 JavaScript Master Class - Interactive Code Tutorial 📚 How It Works This tutorial is designed as a self-paced learning experience where you explore working JavaScript code examples. Unlike traditional tutorials, you learn by examining real implementations and understanding how they work. 🔍 The Learning Method: Execute first - See the workflow in action Open each node - This is where the real learning happens! Study the code - Read JavaScript implementations and comments Understand the flow - See how data transforms between nodes Experiment - Modify code to test your understanding 🎮 The "Game" Concept: It's not a real game - it's a gamified learning experience Uses RPG elements (XP, levels, achievements) to make learning engaging Simulates progression through 3 difficulty levels Main learning happens when you open nodes and read the code!** 🚀 Setup Steps Step 1: Import the Template Copy the JSON template provided Open your n8n instance Create a new workflow Press Ctrl+A (or Cmd+A on Mac) to select all Press Ctrl+V (or Cmd+V) to paste the JSON Click "Save" and name it: JavaScript Master Class - Interactive Tutorial Step 2: Execute the Workflow Click "Test workflow" or "Execute workflow" Watch it run through all nodes automatically See the final results and progression simulation Step 3: Start Learning (The Important Part!) Now the real learning begins - you must open each node manually: 🔍 For Each Code Node: Double-click the node to open it Read the JavaScript code carefully Study the comments - they explain key concepts Understand the logic - how input becomes output Note the techniques used in each challenge 📖 For Each Sticky Note: Read the explanations and context Understand the learning objectives Note the skills being taught 🎯 Learning Path Level 1: Data Warrior (Beginner) 📂 Open Node: 🎲 Level 1: Data Warrior Focus:** Data deduplication using filter() and findIndex() Key Skills:** Array methods, duplicate detection What to Study:** How the deduplication algorithm works Level 2: API Ninja (Intermediate) 📂 Open Node: ⚔️ Level 2: API Ninja Focus:** Data transformation and validation Key Skills:** String manipulation, validation logic, error handling What to Study:** How to clean and validate messy API data Level 3: Automation Master (Advanced) 📂 Open Node: 🏆 Final Boss: Automation Master Focus:** Complex workflow processing Key Skills:** Task orchestration, priority sorting, error handling What to Study:** How to build robust automation systems 💡 Learning Tips 🔍 Active Exploration: Don't just run it** - open every single node! Read all comments** - they contain key insights Compare approaches** - see how complexity increases Try modifications** - change values and see what happens 📝 Study Techniques: Take notes** on patterns you see Copy interesting code** snippets for reference Try to explain** each function to yourself Test your understanding** by modifying the code 🧪 Experimentation: Change filter conditions** in Level 1 Modify validation rules** in Level 2 Adjust workflow logic** in Level 3 Break something** and fix it - great for learning! ⚠️ Important Notes 🎮 "Game" Reality Check: This is NOT an interactive game where you make choices It's a code tutorial with game-like progression themes The "game" runs automatically when executed Real learning happens when you manually open and study each node** 📚 Educational Value: Primary learning:** Understanding JavaScript implementations Secondary learning:** n8n workflow patterns Bonus learning:** Problem-solving approaches 🔧 Technical Requirements: Working n8n instance Basic JavaScript knowledge helpful but not required Willingness to explore and experiment 🎯 Success Metrics You'll know you're learning when you can: ✅ Explain how each deduplication algorithm works ✅ Identify the validation patterns used ✅ Understand the workflow orchestration logic ✅ Modify the code to handle different scenarios ✅ Apply these patterns to your own projects 🤔 Next Steps After completing this tutorial: Apply the patterns to your own workflows Experiment with variations Build something using these techniques Share your learnings with the community Remember: The magic happens when you open each node and study the code! 🔍

Stay ahead of the curve and keep your followers informed—automatically. This n8n workflow uses Perplexity AI to generate insightful answers to scheduled queries, then auto-posts the responses directly to X (Twitter). ⚙️ What this workflow does Scheduled Trigger – Runs at set times (daily, hourly, etc.). searchQuery – Define what kind of trending or relevant insight you want (e.g. “latest AI trends”). set API Key – Securely insert your Perplexity API key. Perplexity API Call – Fetches a short, insightful response to your query. Post to X – Automatically publishes the result as a tweet. 🧩 Requirements An n8n account (self-hosted or cloud) A Perplexity API key A connected X (Twitter) account via n8n’s credentials ✅ Setup Steps Add this workflow into your n8n account. Edit the searchQuery node with a topic (e.g. “What’s new in ecommerce automation?”). Paste your Perplexity API key into the set API key node. Connect your X (Twitter) account in the final node. Adjust the schedule timing to suit your content frequency. 💡 Ideas to Improve 💬 Add a formatting step to shorten or hashtag the response. 📊 Pull multiple trending questions and auto-schedule posts. 🔁 Loop responses to queue a full week of content. 🌐 Translate content before posting to reach a global audience. 🆘 Need help? Feel free to contact us at 1 Node. Get instant access to a library of free resources we created.

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