Workflow Templates

Who is this for? This workflow is designed for: Database administrators and developers working with MongoDB Content managers handling movie databases Organizations looking to implement AI-powered search and recommendation systems Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: The complexity of writing MongoDB aggregation pipelines The need for natural language interaction with movie databases The challenge of maintaining user preferences and favorites The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: Accepts natural language queries about movies Translates user requests into MongoDB aggregation pipelines Queries a movie database containing detailed information including: Plot summaries Genre classifications Cast and director information Runtime and release dates Ratings and awards Provides contextual responses using OpenAI's language model Allows users to save favorite movies to the database Maintains conversation context using a window buffer memory Setup Required Credentials: OpenAI API credentials MongoDB connection details Node Configuration: Configure the MongoDB connection in the MongoDBAggregate node Set up the OpenAI Chat Model with your API key Ensure the webhook trigger is properly configured for receiving chat messages Database Requirements: A MongoDB collection named "movies" with the specified document structure Proper indexes for efficient querying Appropriate user permissions for read/write operations How to customize this workflow Modify the Document Structure: Update the tool description in the MongoDBAggregate node to match your collection schema Adjust the aggregation pipeline templates for your specific use case Enhance the AI Agent: Customize the prompt in the "AI Agent - Movie Recommendation" node Modify the window buffer memory size based on your context needs Add additional tools for more functionality Extend Functionality: Add more MongoDB operations beyond aggregation Implement additional workflows for different types of queries Create custom error handling and validation Add user authentication and rate limiting Integration Options: Connect to external APIs for additional movie data Add webhook endpoints for different platforms Implement caching mechanisms for frequent queries Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

🤖 AI Customer Support Agent with Google Docs Knowledge (Telegram + OpenAI) This no-code workflow turns your Telegram bot into an intelligent, always-on AI support agent that references your business documentation in Google Docs to respond to customer queries—instantly and accurately. Watch full step-by-step video tutorial of the build here: https://youtu.be/Mlv7CjGO7wI 🔧 How it works: Telegram Trigger – Captures incoming messages from users on your Telegram bot Langchain AI Agent (OpenAI GPT) – Interprets the message and uses RAG (retrieval-augmented generation) techniques to craft an answer Google Docs Tool – Connects to and retrieves context from your specified Google Doc (e.g. FAQ, SOPs, policies) Memory Buffer – Keeps track of recent chat history for more human-like conversations Telegram Reply Node – Sends the AI-generated response back to the user 💡 Use Cases: E-commerce customer service SaaS product onboarding Internal helpdesk bot for teams WhatsApp-style support for digital businesses 🧠 What makes this powerful: Supports complex questions by referencing a live Google Doc knowledge base Works in plain conversational language (no buttons or forms needed) Runs 24/7 with zero code Easily extendable to Slack, WhatsApp, or email support 🛠️ Tools used: Telegram Node (trigger + send) Langchain Agent with OpenAI GPT Google Docs Tool Memory Buffer Sticky Notes for easy understanding

Transform raw customer feedback into powerful testimonial quotes automatically. This intelligent n8n workflow monitors feedback forms, uses AI to identify and extract the most emotionally engaging testimonial content, and organizes everything into a searchable database for your marketing campaigns. 🔄 How It Works This streamlined 4-step automation turns feedback into marketing assets: Step 1: Continuous Feedback Monitoring The workflow monitors your Google Sheets (connected to feedback forms) every minute, instantly detecting new customer submissions and triggering the extraction process. Step 2: Intelligent Quote Extraction Google Gemini AI analyzes each feedback submission using specialized prompts designed to: Identify emotionally engaging phrases and statements Extract short, impactful testimonial quotes from longer feedback Filter out neutral, irrelevant, or negative content Focus on marketing-ready, quotable customer experiences Preserve the authentic voice and emotion of the original feedback Step 3: Automated Database Population Extracted testimonials are automatically written back to your Google Sheets in a dedicated "Testimony" column, creating an organized, searchable database of customer quotes ready for marketing use. Step 4: Instant Team Notification Email alerts are sent immediately to your marketing team with each new extracted testimonial, ensuring no valuable social proof goes unnoticed or unused. ⚙️ Setup Steps Prerequisites Google Workspace account for Forms, Sheets, and Gmail Google Gemini API access for intelligent quote extraction n8n instance (cloud or self-hosted) Basic understanding of Google Forms and customer feedback collection Required Google Forms Structure Create a customer feedback form with these essential fields: 📝 Required Form Fields: Name (Short answer text) Email Address (Email field with validation) Feedback (Paragraph text - this is where testimonials are extracted from) Testimony (Leave blank - will be auto-populated by AI) Form Design Best Practices: Use open-ended questions to encourage detailed responses Ask specific questions about customer experience and outcomes Include questions about before/after results for powerful testimonials Make the feedback field prominent and easy to complete Configuration Steps 1. Credential Setup Google Sheets OAuth2**: Monitor feedback responses and update testimonial database Google Gemini API Key**: Extract intelligent, emotionally engaging quotes from feedback Gmail OAuth2**: Send automated notifications to marketing team Google Forms Integration**: Ensure seamless data flow from feedback forms 2. Google Sheets Configuration Verify your feedback response sheet contains proper column structure: | Timestamp | Name | Email | Feedback | Testimony | 3. AI Extraction Optimization The default prompt extracts impactful testimonials, but can be customized for: Industry-Specific Language**: Healthcare, technology, finance, retail terminology Quote Length Preferences**: Short punchy quotes vs longer detailed testimonials Emotional Tone Targeting**: Excitement, relief, satisfaction, transformation Content Focus**: Results-oriented, process-focused, or relationship-based testimonials 4. Notification Customization Email alerts can be configured for: Multiple Recipients**: Marketing team, sales team, customer success Custom Subject Lines**: Include customer name, product type, or urgency indicators Rich Content**: Include full feedback alongside extracted testimonial Categorization**: Different alerts for different product lines or service types 5. Quality Control Implementation Extraction Confidence**: Set minimum quality thresholds for extracted quotes Manual Review Process**: Flag testimonials for human review before publication Approval Workflows**: Add approval steps for high-value or sensitive testimonials Version Control**: Track original feedback alongside extracted quotes 🚀 Use Cases E-commerce & Retail Product Reviews**: Extract compelling quotes from detailed product feedback Customer Success Stories**: Identify transformation narratives from user experiences Social Proof Collection**: Build testimonial libraries for product pages and ads Review Mining**: Turn long reviews into short, shareable testimonial quotes SaaS & Technology Companies User Experience Feedback**: Extract quotes about software usability and impact ROI Testimonials**: Identify statements about business results and efficiency gains Feature Feedback**: Capture specific praise for product capabilities and benefits Customer Success Metrics**: Extract quantifiable results and outcome statements Professional Services Client Success Stories**: Transform project feedback into powerful case study quotes Service Quality Testimonials**: Extract praise for expertise, communication, and results Consulting Impact**: Identify statements about business transformation and growth Relationship Testimonials**: Capture quotes about trust, partnership, and collaboration Healthcare & Wellness Patient Experience**: Extract quotes about care quality and health outcomes Treatment Success**: Identify statements about symptom improvement and recovery Provider Relationships**: Capture testimonials about bedside manner and communication Wellness Journey**: Extract quotes about lifestyle changes and health transformations Education & Training Student Success Stories**: Extract quotes about learning outcomes and career impact Course Effectiveness**: Identify statements about skill development and knowledge gains Instructor Praise**: Capture testimonials about teaching quality and support Career Transformation**: Extract quotes about professional growth and opportunities 🔧 Advanced Customization Options Multi-Category Extraction Enhance extraction with specialized processing: Product-Specific: Extract testimonials for different product lines separately Service-Based: Customize extraction for various service offerings Demographic-Focused: Tailor extraction for different customer segments Journey-Stage: Extract testimonials for awareness, consideration, and retention phases Quality Enhancement Features Implement advanced quality control: Sentiment Scoring**: Rate extracted testimonials for emotional impact Authenticity Verification**: Cross-reference testimonials with customer records Duplicate Detection**: Prevent similar testimonials from the same customer Content Enrichment**: Add context and customer details to extracted quotes Marketing Integration Extensions Connect to marketing and sales tools: Social Media Publishing**: Auto-post testimonials to Facebook, LinkedIn, Twitter Website Integration**: Push testimonials to website testimonial sections Email Marketing**: Include fresh testimonials in newsletter campaigns Sales Enablement**: Provide sales team with relevant testimonials for prospects Analytics and Reporting Generate insights from testimonial data: Testimonial Performance**: Track which quotes generate most engagement Customer Satisfaction Trends**: Analyze testimonial sentiment over time Product/Service Insights**: Identify most praised features and benefits Competitive Advantages**: Extract testimonials highlighting differentiators 📊 Extraction Examples Before (Raw Feedback): "I was really struggling with managing my team's projects and keeping track of all the deadlines. Everything was scattered across different tools and I was spending way too much time just trying to figure out what everyone was working on. Since we started using your project management software about 6 months ago, it's been a complete game changer. Now I can see everything at a glance, our team communication has improved dramatically, and we're actually finishing projects ahead of schedule. The reporting features are amazing too - I can finally show my boss concrete data about our team's productivity. I honestly don't know how we managed without it. The customer support team has been fantastic as well, always quick to help when we had questions during setup." After (AI Extracted Testimonial): "Complete game changer - now I can see everything at a glance, our team communication has improved dramatically, and we're actually finishing projects ahead of schedule." Healthcare Example: Before (Raw Feedback): "I had been dealing with chronic back pain for over 3 years and had tried everything - physical therapy, medication, different doctors. Nothing seemed to help long-term. When I found Dr. Martinez, I was honestly pretty skeptical because I'd been disappointed so many times before. But after our first consultation, I felt hopeful for the first time in years. She really listened to me and explained everything clearly. The treatment plan she developed was comprehensive but manageable. Within just 2 months, I was experiencing significant pain reduction, and now after 6 months, I'm practically pain-free. I can play with my kids again, sleep through the night, and even started hiking on weekends. Dr. Martinez didn't just treat my symptoms - she helped me get my life back." After (AI Extracted Testimonial): "Within just 2 months, I was experiencing significant pain reduction, and now I'm practically pain-free. Dr. Martinez didn't just treat my symptoms - she helped me get my life back." 🛠️ Troubleshooting & Best Practices Common Issues & Solutions Low-Quality Extractions Improve Feedback Questions**: Ask more specific, outcome-focused questions Refine AI Prompts**: Adjust extraction criteria for better quote selection Set Minimum Length**: Ensure feedback has sufficient content for meaningful extraction Quality Scoring**: Implement rating system for extracted testimonials Insufficient Feedback Volume Multiple Feedback Channels**: Collect testimonials through various touchpoints Incentivized Feedback**: Offer small rewards for detailed feedback submissions Follow-up Automation**: Send feedback requests to satisfied customers Timing Optimization**: Request feedback at optimal moments in customer journey Privacy and Consent Issues Permission Management**: Ensure customers consent to testimonial use Attribution Control**: Allow customers to specify how they want to be credited Approval Workflows**: Implement customer approval before publishing testimonials Data Protection**: Maintain compliance with privacy regulations Optimization Strategies Extraction Quality Enhancement Prompt Engineering**: Continuously refine AI prompts based on output quality A/B Test Extractions**: Test different extraction approaches for effectiveness Human Review Integration**: Combine AI extraction with human editorial oversight Context Preservation**: Maintain customer context alongside extracted quotes Marketing Integration Campaign Alignment**: Extract testimonials that support specific marketing campaigns Audience Segmentation**: Categorize testimonials for different target audiences Channel Optimization**: Format testimonials for specific marketing channels Performance Tracking**: Monitor which testimonials drive best marketing results Process Automation Multi-Stage Processing**: Implement multiple extraction and refinement steps Quality Gates**: Add checkpoints for testimonial quality and relevance Workflow Branching**: Route different types of feedback to appropriate processes Error Handling**: Implement fallbacks for failed extractions or poor-quality feedback 📈 Success Metrics Extraction Efficiency Processing Speed**: Reduce time from feedback submission to usable testimonial Success Rate**: Percentage of feedback submissions yielding quality testimonials Quote Quality**: Average rating of extracted testimonials by marketing team Volume Increase**: Growth in testimonial collection and database size Marketing Impact Testimonial Usage**: Frequency of extracted testimonials in marketing campaigns Conversion Rates**: Impact of AI-extracted testimonials on sales metrics Social Proof Effectiveness**: Engagement rates on testimonial-based content Customer Acquisition**: Attribution of new customers to testimonial-driven campaigns 📞 Questions & Support Need help implementing your AI Testimonial Extractor Agent? 📧 Specialized Technical Support Email**: Yaron@nofluff.online Response Time**: Within 24 hours on business days Expertise**: AI testimonial extraction, feedback form optimization, marketing automation 🎥 Comprehensive Learning Library YouTube Channel**: https://www.youtube.com/@YaronBeen/videos Complete setup guides for feedback form design and AI extraction Advanced prompt engineering techniques for testimonial quality Integration tutorials for marketing platforms and social media Best practices for customer feedback collection and testimonial usage Troubleshooting common extraction and quality issues 🤝 Professional Marketing Community LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Connect for ongoing testimonial marketing automation support Share your customer success story automation achievements Access exclusive templates for feedback forms and testimonial campaigns Join discussions about social proof marketing and customer experience automation 💬 Support Request Guidelines Include in your support message: Your industry and typical customer feedback patterns Current testimonial collection process and challenges Specific marketing channels where testimonials will be used Volume expectations and quality requirements Integration needs with existing marketing tools Ready to turn every customer feedback into marketing gold? Deploy this AI Testimonial Extractor Agent and build a powerful testimonial database that drives sales and builds trust with prospects automatically!

Who is this for? This workflow is ideal for businesses, accountants, and finance teams who receive bank slip images via LINE and want to automate the extraction of transaction details. It eliminates manual data entry and speeds up financial tracking. What problem does this workflow solve? Many businesses receive bank transfer slips via LINE from customers, but manually recording transaction details into spreadsheets is time-consuming and error-prone. This workflow automates the entire process, extracting structured data from the bank slips and storing it in Google Sheets for seamless record-keeping. What this workflow does: Receives bank slip images from LINE BOT Extracts transaction details (sender, receiver, amount, transaction ID) using SpaceOCR Automatically logs extracted data into Google Sheets Works with Standard Bank Slips & PromptPay transactions Eliminates manual data entry and reduces errors Setup Instructions: 1. Prerequisites A LINE BOT with Messaging API enabled A SpaceOCR API Key (Get from https://spaceocr.com/) A Google Sheets account to store extracted data An n8n instance running (Cloud or Self-hosted) 2. Setup Google Sheets Create a Google Sheet with the following structure: A (Date) B (Time) C (Sender) D (Receiver) E (Bank Name) F (Amount) G (Transaction ID) Ensure your Google Sheets API is enabled and connected to n8n. For an example of the required format, check this Google Sheets template: Google Sheets Template 3. Configure n8n Workflow 1. Webhook Node (Receives bank slip from LINE BOT) Set method:* Set Path:* 2. HTTP Request (Download Image from LINE Message) Retrieves image URL from the LINE message payload 3. SpaceOCR Node (Extract Text from Bank Slip) Input:* API Key:* #### 4. Google Sheets Node (Save Transaction Data) Select your Google Sheet Map extracted data (sender, receiver, amount, etc.) to the respective columns 4. Deploy & Test Activate the workflow in n8n Set Webhook URL in LINE Developer Console Send a test bank slip image to the LINE BOT Check Google Sheets for extracted transaction data

Who is this template for? This workflow template is designed for people seeking alerts when certain specific changes are made to any web page. Leveraging agentic AI, it analyzes the page every day and autonomously decides whether to send you an e-mail notification. Example use cases Track price changes on [competitor's website]. Notify me when the price drops below €50. Monitor new blog posts on [industry leader's website] and summarize key insights. Check [competitor's job page] for new job postings related to software development. Watch for new product launches on [e-commerce site] and send me a summary. Detect any changes in the terms and conditions of [specific website]. Track customer reviews for [specific product] on [review site] and extract key themes. How it works When clicking 'test workflow' in the editor, a new browser tab will open where you can fill in the details of your espionage assignment Make sure you be as concise as possible when instructing AI. Instruct specific and to the point (see examples at the bottom). After submission, the flow will start off by extracting both the relevant website url and an optimized prompt. OpenAI's structured outputs is utilized, followed by a code node to parse the results for further use. From here on, the endless loop of daily checks will begin: Initial scrape 1 day delay Second scrape AI agent decides whether or not to notify you Back to step 1 You can cancel an espionage assignment at any time in the executions tab Set up steps Insert your OpenAI API key in the structured outputs node (second one) Create a Firecrawl account and connect your Firecrawl API key in both 'Scrape page'-nodes Connect your OpenAI account in the AI agents' model node Connect your Gmail account in the AI agents' Gmail tool node

Overview This automated workflow fetches Upwork job postings using Apify, removes duplicate job listings via Airtable, and sends new job opportunities to Slack. Key Features: Automated job retrieval** from Upwork via Apify API Duplicate filtering** using Airtable to store only unique jobs Slack notifications** for new job postings Runs every 30 minutes** during working hours (9 AM - 5 PM) This workflow requires an active Apify subscription to function, as it uses the Apify Upwork API to fetch job listings. Who is This For? This workflow is ideal for: Freelancers looking to track Upwork jobs in real time Recruiters automating job collection for analytics Developers who want to integrate Upwork job data into their applications What Problem Does This Solve? Manually checking Upwork for jobs is time-consuming and inefficient. This workflow: Automates job discovery based on your keywords Filters out duplicate listings, ensuring only new jobs are stored Notifies you on Slack when new jobs appear How the Workflow Works 1. Schedule Trigger (Every 20 Minutes) Triggers the workflow at 20-minute intervals Ensures job searches are only executed during working hours (9 AM - 5 PM) 2. Query Upwork for Jobs Uses Apify API to scrape Upwork job posts for specific keywords (e.g., "n8n", "Python") 3. Find Existing Jobs in Airtable Searches Airtable to check if a job (based on title and link) already exists 4. Filter Out Duplicate Jobs The Merge Node compares Upwork jobs with Airtable data The IF Node filters out jobs that are already stored in the database 5. Save Only New Jobs in Airtable The Insert Node adds only new job listings to the Airtable collection 6. Send a Slack Notification If a new job is found, a Slack message is sent with job details Setup Guide Required API Keys Upwork Scraper (Apify Token) – Get your token from Apify Airtable Credentials Slack API Token – Connect Slack to n8n and set the channel ID (default: #general) Configuration Steps Modify search keywords in the 'Assign Parameters' node (startUrls) Adjust the Working Hours in the 'If Working Hours' node Set your Slack channel in the Slack node Ensure Airtable is connected properly - you'll need to create a table with 'title' and 'link' columns. Adjust the 'If Working Hours' node to match your timezone and hours, or remove it altogether to receive notifications and updates constantly. How to Customize the Workflow Change keywords: update the startUrls in the 'Assign Parameters' node to track different job categories Change 'If Working Hours': Modify conditions in the IF Node to filter times based on your needs Modify Slack Notifications: Adjust the Slack message format to include additional job details Why Use This Workflow? Automated job tracking without manual searches Prevents duplicate entries in Airtable Instant Slack notifications for new job opportunities Customizable – adapt the workflow to different job categories Next Steps Run the workflow and test with a small set of keywords Expand job categories for better coverage Enhance notifications by integrating Telegram, Email, or a dashboard This workflow ensures real-time job tracking, prevents duplicates, and keeps you updated effortlessly.

Generate and Publish AI Content to LinkedIn and X (Twitter) with n8n Overview This n8n workflow automates the generation and publishing of AI-powered social media content across LinkedIn and X (formerly Twitter). By leveraging AI, this workflow helps social media managers, marketers, and content creators streamline their posting process. Who is this for? Social media managers Content creators Digital marketers Businesses looking to automate content generation Features AI-powered content creation** tailored for LinkedIn and X (Twitter) Automated publishing** to both platforms Structured output parsing** to ensure consistency OAuth2 authentication** for secure posting Merge and confirmation steps** to track successful postings Setup Instructions Prerequisites Before using this workflow, ensure you have: An n8n instance set up API credentials for: Google Gemini AI (for content generation) X Developer Account with OAuth2 authentication LinkedIn Developer Account with OAuth2 authentication A form submission service integrated with n8n Workflow Breakdown 1. Trigger: Form Submission A user submits a form containing the post title. The form is secured with Basic Authentication. The submitted title is passed to the AI Agent. 2. AI Content Generation The Google Gemini Chat Model processes the title and generates: LinkedIn post content Twitter (X) post content Hashtags Call-to-action (LinkedIn) Character limit check (Twitter) 3. Parsing AI Output A structured output parser converts the AI-generated content into a JSON format. Ensures correct formatting for LinkedIn and Twitter (X). 4. Publishing to Social Media X (Twitter) Posting Extracts the Twitter post from the AI output. Publishes it via an OAuth2-authenticated X (Twitter) account. LinkedIn Posting Extracts the LinkedIn post from the AI output. Publishes it via an OAuth2-authenticated LinkedIn account. 5. Merging Post Results Merges the response data from both LinkedIn and Twitter after publishing. 6. Confirmation Step Displays a final confirmation form once the posts are successfully published. Benefits Save time** by automating content creation and publishing. Ensure consistency** across platforms with structured AI-generated posts. Secure authentication** using OAuth2 for LinkedIn and Twitter. Increase engagement** with AI-optimized hashtags and CTAs. This workflow enables seamless social media automation, helping professionals post engaging AI-powered content effortlessly. 🚀

Built this for a dedicated Slack outage-notifications channel — works well on both desktop and mobile. This is for: IT Administrators & small MSPs looking to streamline M365 alerts from one or multiple mailboxes into a single or specific Slack channels IT Admins who prefer ChatOps over management-by-email What does it do Scans for M365 outage alerts emails (every 1 min) Checks if it impacts a specific user region (if the alert calls it out, countries have to be manually set) Summarizes the incident using OpenAI o4-mini (cheap model - or you can swap for local Ollama) Sends a Slack Block to your outage channel with incident link (can be extended) Deletes the original alert email after successful delivery Credentials Outlook: Create an Outlook credential (OAuth2.0) to point to the mailbox (regular or shared) where M365 service alerts will be received Slack: Create a Slack bot credential with access to the slack channel you want updates posted to OpenAI: Create a OpenAI credential that has access to the GPT-4O-MINI model. Recommend you use projects in OpenAI so that you may set a per-project-budget and not impact other projects. Review this OpenAI documentation for more info on managing Projects in the API portal. Expect this to consume no more than 1-2 cents per month on average. Setup Download & import the workflow Modify the first Outlook block (Check for 365 Service Alert) to use the Outlook credential Modify the OpenAI block's system prompt to call out the countries your users reside in ie. "- Assume the organization has users primarily in the U.S. and Australia. If those regions are affected, state: "Your users may have been affected." Otherwise, add: "No impact expected for your user base."" ← swap U.S. & Australia for desired countries Modify the Slack block (Post outage to Slack) to specify the channel updates will be posted to Sample Slack Output Workflow Diagram

A Discord bot that responds to mentions by sending messages to n8n workflows and returning the responses. Connects Discord conversations with custom automations, APIs, and AI services through n8n. Full guide on: https://github.com/JimPresting/AI-Discord-Bot/blob/main/README.md Discord Bot Summary Overview The Discord bot listens for mentions, forwards questions to an n8n workflow, processes responses, and replies in Discord. This workflow is intended for all Discord users who want to offer AI interactions with their respective channels. What do you need? You need a Discord account as well as a Google Cloud Project Key Features 1. Listens for Mentions The bot monitors Discord channels for messages that mention it. Optional Configuration**: Can be set to respond only in a specific channel. 2. Forwards Questions to n8n When a user mentions the bot and asks a question: The bot extracts the question. Sends the question, along with channel and user information, to an n8n webhook URL. 3. Processes Data in n8n The n8n workflow receives the question and can: Interact with AI services (e.g., generating responses). Access databases or external APIs. Perform custom logic. n8n formats the response and sends it back to the bot. 4. Replies to Discord with n8n's Response The bot receives the response from n8n. It replies to the user's message in the Discord channel with the answer. Long Responses**: Handles responses exceeding Discord's 2000-character limit by chunking them into multiple messages. 5. Error Handling Includes error handling for: Issues with n8n communication. Response formatting problems. Manages cases where: No question is asked. An invalid response is received from n8n. 6. Typing Indicator While waiting for n8n's response, the bot sends a "typing..." indicator to the Discord channel. 7. Status Update For lengthy n8n processes, the bot sends a message to the Discord channel to inform the user that it is still processing their request. Step-by-Step Setup Guide as per Github Instructions Key Takeaways You’ll configure an n8n webhook to receive Discord messages, process them with your workflow, and respond. You’ll set up a Discord application and bot, grant the right permissions/intents, and invite it to your server. You’ll prepare your server environment (Node.js), scaffold the project, and wire up environment variables. You’ll implement message‐chunking, “typing…” indicators, and robust error handling in your bot code. You’ll deploy with PM2 for persistence and know how to test and troubleshoot common issues. 1. n8n: Create & Expose Your Webhook New Workflow Log into your n8n instance. Click Create Workflow (➕), name it e.g. Discord Bot Handler. Webhook Trigger Add a node (➕) → search Webhook. Set: Authentication: None (or your choice) HTTP Method: POST Path: e.g. /discord-bot Click Execute Node to activate. Copy Webhook URL After execution, copy the Production Webhook URL. You’ll paste this into your bot’s .env. Build Your Logic Chain additional nodes (AI, database lookups, etc.) as required. Format the JSON Response Insert a Function node before the end: return { json: { answer: "Your processed reply" } }; Respond to Webhook Add Respond to Webhook as the final node. Point it at your Function node’s output (with the answer field). Activate Toggle Active in the top‐right and Save. 2. Discord Developer Portal: App & Bot New Application Visit the Discord Developer Portal. Click New Application, name it. Go to Bot → Add Bot. Enable Intents & Permissions Under Privileged Gateway Intents, toggle Message Content Intent. Under Bot Permissions, check: Read Messages/View Channels Send Messages Read Message History Grab Your Token In Bot → click Copy (or Reset Token). Store it securely. Invite Link (OAuth2 URL) Go to OAuth2 → URL Generator. Select scopes: bot, applications.commands. Under Bot Permissions, select the same permissions as above. Copy the generated URL, open it in your browser, and invite your bot. 3. Server Prep: Node.js & Project Setup Install Node.js v20.x sudo apt purge nodejs npm sudo apt autoremove curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash - sudo apt install -y nodejs node -v # Expect v20.x.x npm -v # Expect 10.x.x Project Folder mkdir discord-bot cd discord-bot Initialize & Dependencies npm init -y npm install discord.js axios dotenv 4. Bot Code & Configuration Environment Variables Create .env: nano .env Populate: DISCORD_BOT_TOKEN=your_bot_token N8N_WEBHOOK_URL=https://your-n8n-instance.com/webhook/discord-bot Optional: restrict to one channel TARGET_CHANNEL_ID=123456789012345678 Bot Script Create index.js: nano index.js Implement: Import dotenv, discord.js, axios. Set up client with MessageContent intent. On messageCreate: Ignore bots or non‐mentions. (Optional) Filter by channel ID. Extract and validate the user’s question. Send “typing…” every 5 s; after 20 s send a status update if still processing. POST to your n8n webhook with question, channelId, userId, userName. Parse various response shapes to find answer. If answer.length ≤ 2000, message.reply(answer). Else, split into ~1900‑char chunks at sentence/paragraph breaks and send sequentially. On errors, clear intervals, log details, and reply with an error message. Login client.login(process.env.DISCORD_BOT_TOKEN); 5. Deployment: Keep It Alive with PM2 Install PM2 npm install -g pm2 Start & Monitor pm2 start index.js --name discord-bot pm2 status pm2 logs discord-bot Auto‐Start on Boot pm2 startup Follow the printed command (e.g. sudo env PATH=$PATH:/usr/bin pm2 startup systemd -u your_user --hp /home/your_user) pm2 save 6. Test & Troubleshoot Functional Test In your Discord server: @YourBot What’s the weather like? Expect a reply from your n8n workflow. Common Pitfalls No reply → check pm2 logs discord-bot. Intent Errors → verify Message Content Intent in Portal. Webhook failures → ensure workflow is active and URL is correct. Formatting issues → confirm your Function node returns json.answer. Inspect Raw Data Search your logs for Complete response from n8n: to debug payload shapes. `

Description This intelligent n8n automation streamlines the process of collecting, extracting, and scoring resumes sent to a Gmail inbox—making it an ideal solution for recruiters who regularly receive hundreds of applications. The workflow scans incoming emails with attachments, extracts relevant candidate information from resumes using AI, evaluates each candidate based on customizable criteria, and logs their scores alongside contact details in a connected Google Sheet. Who Is This For? Recruiters & Hiring Managers**: Automate the resume screening process and save hours of manual work. HR Teams at Startups & SMBs**: Quickly evaluate talent without needing large HR ops infrastructure. Agencies & Talent Acquisition Firms**: Screen large volumes of resumes efficiently and with consistent criteria. Solo Founders Hiring for Roles**: Use AI to help score and shortlist top candidates from email applications. What Problem Does This Workflow Solve? Manually reviewing resumes is time-consuming, error-prone, and inconsistent. This workflow solves these challenges by: Automatically detecting and extracting resumes from Gmail attachments. Using OpenAI to intelligently extract candidate info from unstructured PDFs. Scoring resumes using customizable evaluation criteria (e.g., relevant experience, skills, education). Logging all candidate data (Name, Email, LinkedIn, Score) in a centralized, filterable Google Sheet. Enabling faster, fairer, and more efficient candidate screening. How It Works 1. Gmail Trigger Runs on a scheduled interval (e.g., every 6 or 24 hours). Scans a connected Gmail inbox (using OAuth credentials) for unread emails that contain PDF attachments. 2. Extract Attachments Downloads the attached resumes from matching emails. 3. Parse Resume Text Sends the PDF file to OpenAI's API (via GPT-4 or GPT-3.5 with file support or via base64 + PDF-to-text tool). Prompts GPT with a structured format to extract fields like Name, Email, LinkedIn, Skills, and Education. 4. Score Resume Evaluates the resume on predefined scoring logic using AI or logic inside the workflow (e.g., "Has X skill = +10 points"). 5. Log to Google Sheets Appends a new row in a connected Google Sheet, including: Candidate Name Email Address LinkedIn URL Resume Score Setup Accounts & API Keys You’ll need accounts and credentials for: n8n** (hosted or self-hosted) Google Cloud Platform** (for Gmail, Drive, and Sheets APIs) OpenAI** (for GPT model access) Google Sheet Make a Google Sheet and connect it via Google Sheets node in n8n. Columns should include: Name Email LinkedIn Score Configuration Google Cloud: Enable Gmail API and Google Sheets API. Set up OAuth 2.0 Credentials in Google Console. Connect n8n Gmail, Drive, and Sheets nodes to these credentials. OpenAI: Generate an API Key. Use the HTTP Request node or official OpenAI node to send prompt requests. n8n Workflow: Add Gmail Trigger. Add extraction logic (e.g., filter PDFs). Add OpenAI prompt for resume parsing and scoring. Connect structured output to a Google Sheets node. Requirements Accounts: n8n** Google** (Gmail, Sheets, Drive, Cloud Console) OpenAI** API Keys & Credentials: OpenAI API Key Google Cloud OAuth Credentials Gmail Access Scopes (for reading attachments) Configured Google Sheet OpenAI usage (after free tier) Google Cloud API usage (if exceeding free quota)

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs

Who is this for? This workflow is for digital marketers, small business owners, lead generation agencies, and VAs who need a scalable way to find and store local business leads using AI. It’s especially useful for teams that want to enrich leads with real-time news insights and save the structured data to Airtable. What problem is this workflow solving? Manually researching local businesses and staying up to date with relevant news is time-consuming and inefficient. This automation eliminates that burden by using Dumpling AI chat agents to generate leads and context, GPT-4o to summarize, and Airtable to store everything in one place. What this workflow does This AI workflow listens for a manual trigger in n8n and executes the following steps: Extracts local business leads using a Local Business Agent from Dumpling AI. Pulls current news related to the business type or location using a News Agent from Dumpling AI. Uses GPT-4o to combine both responses into a human-readable summary. Extracts structured lead data like name, category, and city. Saves the summary and lead data into Airtable for easy follow-up. Setup 1. Create AI Agents in Dumpling AI Sign in at Dumpling AI Create two separate agents: Local Business Agent: Designed to respond with structured lists of businesses by location and category. News Agent: Designed to fetch relevant recent news and summaries about a specific industry or region. After setting up each agent, copy the Agent Key from Dumpling AI. These keys will be required in the headers of your HTTP Request nodes in n8n. 2. Manual Trigger This workflow begins with a manual trigger inside n8n, Which is the When chat message is recieved. This makes it easy to test and reuse, especially during setup. 3. Get Local Business Data from Dumpling AI The first HTTP Request node sends a prompt like List 5 top real estate companies in Atlanta with full address and services. Include your Local Business Agent Key in the x-agent-key header. The response will return a structured list of business leads. 4. Get News Context from Dumpling AI The second HTTP Request node sends a prompt such as Give me the latest news related to the real estate market in Atlanta. Use your News Agent Key in the header. This fetches a brief set of recent news summaries relevant to the businesses being researched. 5. Use GPT-4o to Merge and Summarize The GPT node combines the list of businesses and news into one coherent summary. You can modify the prompt to output in paragraph format, bullet points, or structured notes. 6. Save Lead to Airtable The Airtable node sends all structured fields into your selected base and table. Be sure to connect your Airtable account and confirm the columns match exactly. How to customize this workflow Replace the prompt inside the HTTP node to focus on different types of businesses or cities. Expand the GPT output to include additional lead info like websites, phone numbers, or emails if the agent includes them. Add a webhook trigger to allow this flow to be run via a chatbot, external app, or button. Link to HubSpot or another CRM to sync the leads automatically. Duplicate the process to run for multiple industries in parallel. Final Notes You must create and configure your Dumpling AI agents first before running this workflow. The Agent Keys from Dumpling AI are required in both HTTP Request nodes. This flow is modular and flexible, ready for deeper CRM integrations. The manual trigger is great for testing, but you can add a Webhook node to automate it. This workflow helps you launch an intelligent lead gen process that combines location-targeted business discovery, AI-generated insights, and structured CRM-friendly output, all powered by Dumpling AI and OpenAI.