Workflow Templates

AI-Agent Code Review for GitHub Pull Requests Description: This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet). It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI. 🔧 What It Does Triggered on PR creation Extracts code diffs from the PR Formats and feeds them into an OpenAI prompt Enriches the prompt using a Google Sheet of Swift best practices Posts an AI-generated review as a comment on the PR Applies a PR label to visually mark reviewed PRs ✅ Prerequisites Before deploying this workflow, ensure you have the following: n8n Instance (Self-hosted or Cloud) GitHub Repository with PR activity OpenAI API Key** for GPT-4o, GPT-4-turbo, or GPT-3.5 GitHub OAuth App** (or PAT) connected to n8n to post comments and access PR diffs (Optional) Google Sheets API credentials if using the code best practices lookup node. ⚙️ Setup Instructions 1. Import the Workflow in n8n, click on Workflows → Import from file or JSON Paste or upload the JSON code of this template 2. Configure Triggers and Connections 🔁 GitHub Trigger Node**: PR Trigger Repository**: Select the GitHub repo(s) to monitor Events**: Set to pull_request Auth**: Use GitHub OAuth2 credentials 📥 HTTP Request Node: Get file's Diffs from PR No authentication needed; it uses dynamic path from trigger 🧠 OpenAI Model Node**: OpenAI Chat Model Model**: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo Credential**: Provide your OpenAI API Key 🧑‍💻 Code Review Agent Node : Code Review Agent Connected to OpenAI and optionally to tools like Google Sheets 💬 GitHub Comment Poster Uses GitHub API to post review comments back on PR Node: GitHub Robot Credential: Use the agent Github account (OAuth or PAT) Repo : Pick your owen Github Repository 🏷️ PR Labeler (optional) Adds label ReviewedByAI after successful comment Node: Add Label to PR Label : you ca customize the label text of your owen tag. 📊 Google Sheet Best Practices config (optional) Connects to a Google Sheet for coding guideline lookups, we can replace Google sheet by another tool or data base First prepare your best practices list with the clear description and the code bad/good examples Add al the best practices in your Google Sheet Configure* the Code *Best Practices node** in the template : Credential : Use your Google Sheet account by OAuth2 URL : Add your Google Sheet document URL Sheet : Add the name of the best practices sheet

Idea The idea for app came since I wanted to build a unique gift for my niece because she gets excited for her birthday (which Im going to miss this year). The web app has a simple countdown (in html and JS) but more importantly, there is an AI agent that will answer some specific questions and know her preferences. How it works The questions from app are sent via web hook to N8N which has pulls preferences file (about her likes, dislikes, personality) from postgre and AI Agent that will answer questions/respond. The current status is stored back in postgre (especially about status of cat and universe happenings) before responding back. Features Integrated AI chatbot via N8N webhook Persistent conversation history Minimizable chat interface Fallback support for offline testing Features: -- Wheres Mittens - This is a query to track her lost cat in multiverse. -- Multiverse updates with recent update stored Pre Requisites Postgre SQL database is available. Alternatively, use any other database but change the N8N nodes. LLM Api Key. Step by Step Instructions Export this N8N Workflow. Modify LLM API Key, I used openAI, 4.1 For web app scofflding,you will need Node, HTML and Javascript. I've created a mini version using Node and JS with web app and N8N connection settings here: <https://github.com/productiser/FiBirthdayAgent> PostgreSQL Database Script (1 table for memory and context storage): CREATE TABLE fifi_world_context ( id TEXT PRIMARY KEY, -- e.g., 'agent_fifi' cat_location TEXT, -- e.g., "Bubble Nebula" cat_activity TEXT, -- e.g., "Playing laser tag with moon mice" fifi_preferences JSONB, -- e.g., likes/dislikes/foods/shows world_history TEXT, -- Summary of narrative events last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); 5.Modify system prompt as per your needs. Built With N8N Self hosted Self hosted web app Hosted on Vercel Total spend = <£1 (AI costs only) Total Time = <1 day Support Watch this video for web app overview and how it looks. <https://youtu.be/e7PlrTdvwoM> Contact me on info@pankstr.com/ superllmuser@gmail.com for any queries Hope you enjoy!!

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response Listens for incoming user questions (can be extended to webhook). Converts questions to vector embeddings and performs similarity search on MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce direct, context-aware answers. Maintains short-term conversation context using a memory buffer node. Setup Setting up vector embeddings Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. Setting up chat Configure the AI system prompt in the “Knowledge Base Agent” node to reflect your company’s tone, answering style, and any business rules. Update the workflow description and instructions to help users understand the chat’s purpose and capabilities. Connect the MongoDB collection used for vector search in the chat workflow and update the vector search index if needed to match your setup. Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection, with: An embedding field storing vector data, Relevant metadata fields (e.g., document ID, source), and The same vector index name configured (e.g., data_index). Search Index Example: { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }

This n8n workflow automates website security audits. It combines direct website scanning, threat intelligence from AlienVault OTX, and advanced analysis from an OpenAI large language model (LLM) to generate and email a comprehensive security report. How it Works (Workflow Flow): Input: A user provides a website URL via a simple web form. Data Collection: An HTTP Request node visits the provided URL to gather initial data (status code, headers). An AlienVault HTTP Request node queries AlienVault OTX for known threats associated with the website's hostname. Data Preparation (Prepare Data for AI): A custom code node consolidates the collected website data and AlienVault intelligence, performing initial checks for common issues (e.g., error codes, missing security headers, AlienVault warnings). AI Analysis (Security Configuration Audit): The prepared data is sent to an OpenAI Chat Model, which acts as a cybersecurity expert. The AI analyzes the data to identify vulnerabilities, explain their impact, suggest exploitation methods, and outline mitigation steps. Report Formatting (Format Report for Email): Another custom code node takes the AI's plain-text report and converts it into a structured HTML format suitable for email. Delivery (Send Security Report): The final HTML report is sent via Gmail to a specified email address. Setup Steps: To use this workflow, you'll need an n8n instance and the following credentials: n8n Instance: Ensure your n8n environment is running. OpenAI API Key: Generate a key from OpenAI. Add an "OpenAI API" credential in n8n (e.g., "OpenAI account"). AlienVault OTX API Key: Obtain a key from your AlienVault OTX profile. Add an "AlienVault OTX API" credential in n8n (e.g., "AlienVault account"). Gmail Account: Set up a "Gmail OAuth2" credential in n8n for sending emails (recommended for security; involves Google Cloud setup). Import Workflow: Copy the workflow's JSON code. In n8n, import the workflow via "Workflows" > "New" > "Import from JSON". Configure Recipient: In the "Send Security Report" node, specify the email address where reports should be sent. Activate: Enable the workflow to start processing submissions. Once activated, access the "On form submission" webhook URL to input a URL and trigger an audit.

🧾 What this workflow does This workflow turns YouTube video links into ready-to-edit newsletter drafts using Dumpling AI and GPT-4o. It reads new video URLs from a Google Sheet, extracts their transcripts, summarizes them into email-friendly content, and logs the finished draft back into the same sheet. An email notification is also sent to alert the user once each draft is created. 👤 Who is this for Newsletter writers or marketers repurposing video content YouTube creators building email follow-ups from videos Agencies or VAs batching social → email content Automation users streamlining content workflows ⚙️ How to set up ✅ Requirements Google Sheet** with the following columns: link — YouTube video URL blog post — for saving the generated newsletter draft Active accounts for: Dumpling AI (API for YouTube transcripts) OpenAI GPT-4 or GPT-4o Google Sheets Gmail (OAuth2 credential) 🔧 Setup steps Connect all credentials using n8n's Credential Manager: Google Sheets (OAuth2) Dumpling AI (via HTTP Header Auth) OpenAI Gmail Update the sheet ID and tab name in both Google Sheets nodes. Customize the GPT-4o prompt (optional): Located in the “GPT-4o: Write Newsletter Draft from Transcript” node You can edit tone, structure, and audience targeting in the system message Verify email recipient in the Gmail node and update if needed. 🧠 How it works The workflow is triggered manually or on schedule. It pulls YouTube links without drafts from the sheet. Each video’s transcript is fetched using Dumpling AI. GPT-4o summarizes the transcript into a clean, friendly newsletter format. The draft is written back to the same row in Google Sheets. An email is sent to notify the user that the draft is ready. 🛠️ Customization ideas Send finished drafts to Notion or Airtable instead of Sheets Generate social media posts from the same transcript Add automatic review steps using GPT scoring or editing Trigger this on new form submissions or YouTube uploads instead This is a fast, AI-powered way to turn long-form video content into clean, polished newsletters — ready to share or schedule with minimal editing.

🎯 What It Does: This project lets you talk to Siri (via Apple Shortcuts) and record or query your daily spending. The shortcut sends your message to an n8n Webhook, which uses AI to decide whether it’s for writing or reading finance data, then replies with a human-friendly message — all powered by n8n + AI + Google Sheets. ⸻ 🌐 PART 1: n8n Setup 🧩 1. Create a Webhook Trigger in n8n • Add a node: Webhook • Set HTTP Method: POST • Set Path: siri-finance • Enable “Respond to Webhook” = ✅ 🧠 2. Add AI Agent Node (e.g. OpenAI, Ollama, Gemini) • Use system prompt like: You are a finance assistant. Decide if the user wants to record or read transactions. If it's recording, return a JSON object with date, type, name, amount, and expense/income. If it's reading, return date range and type (Expense/Income). Always reply with a human-friendly summary. • Input: {{ $json.text }} (from webhook) • Output: structured json.output 🧮 3. (Optional) Add Logic to write to DB / Supabase / Google Sheets • Append tool: Adds a new row • Read tool: Queries past data Now your n8n flow is ready! ⸻ 📱 PART 2: iOS Shortcut Setup ⚙️ 1. Create a new Shortcut • Name it: 記帳助理 (or Finance Bot) • Add Action: Ask for Input • Prompt: “請說出你的記帳內容” • Input Type: Text • Add Action: Get Contents of URL • Method: POST • URL: https://your-n8n-domain/webhook/siri-finance • Headers: Content-Type: application/json • Request Body: { "text": "Provided Input" } • Replace "Provided Input" with Magic Variable → Input Result 🔊 2. Show Result • Add Action: Show Result • Content: Get Contents of URL 🗣️ 3. Optional: Add “Speak Text” • If you want Siri to speak it back, add Speak Text after Show Result. ⸻ ✅ Example Usage • You: “Hey Siri, 開支$50 早餐” • Siri: “已記錄支出：項目 早餐，金額 $50，已寫入” Or • You: “查一下我過去7日用了幾多錢” • Siri: “你過去7日總支出為 $7684.64，包括：⋯⋯” ⸻ 📦 Files to Share You can package the following: • .shortcut file export • Sample n8n workflow .json • Optional Supabase schema / Google Sheet template ⸻ 💡 Tips for Newcomers • Keep your Webhook public but protect with token if needed. • Ensure you handle emoji and newline safely for iOS compatibility. • Add logging nodes in n8n to help debug Siri messages. ⸻ 🗣️ Optional Project Name “Siri 記帳助理” / “Finance VoiceBot” A simple voice-first way to manage your daily expenses.

Workflow Overview This cutting-edge n8n automation is a sophisticated market research and intelligence gathering tool designed to transform web content discovery into actionable insights. By intelligently combining web crawling, AI-powered filtering, and smart summarization, this workflow: Discovers Relevant Content: Automatically crawls target websites Identifies trending topics Extracts comprehensive article details Intelligent Content Filtering: Applies custom keyword matching Filters for most relevant articles Ensures high-quality information capture AI-Powered Summarization: Generates concise, meaningful summaries Extracts key insights Provides quick, digestible information Seamless Delivery: Sends summaries directly to Slack Enables instant team communication Facilitates rapid information sharing Key Benefits 🤖 Full Automation: Continuous market intelligence 💡 Smart Filtering: Precision content discovery 📊 AI-Powered Insights: Intelligent summarization 🚀 Instant Delivery: Real-time team updates Workflow Architecture 🔹 Stage 1: Content Discovery Scheduled Trigger**: Daily market research FireCrawl Integration**: Web content crawling Comprehensive Site Scanning**: Extracts article metadata Captures full article content Identifies key information sources 🔹 Stage 2: Intelligent Filtering Keyword-Based Matching** Relevance Assessment** Custom Domain Optimization**: AI and technology focus Startup and innovation tracking 🔹 Stage 3: AI Summarization OpenAI GPT Integration** Contextual Understanding** Concise Insight Generation**: 3-point summary format Captures essential information 🔹 Stage 4: Team Notification Slack Integration** Instant Information Sharing** Formatted Insight Delivery** Potential Use Cases Market Research Teams**: Trend tracking Innovation Departments**: Technology monitoring Startup Ecosystems**: Competitive intelligence Product Management**: Industry insights Strategic Planning**: Rapid information gathering Setup Requirements FireCrawl API Web crawling credentials Configured crawling parameters OpenAI API GPT model access Summarization configuration API key management Slack Workspace Channel for insights delivery Appropriate app permissions Webhook configuration n8n Installation Cloud or self-hosted instance Workflow configuration API credential management Future Enhancement Suggestions 🤖 Multi-source crawling 📊 Advanced sentiment analysis 🔔 Customizable alert mechanisms 🌐 Expanded topic tracking 🧠 Machine learning refinement Technical Considerations Implement robust error handling Use exponential backoff for API calls Maintain flexible crawling strategies Ensure compliance with website terms of service Ethical Guidelines Respect content creator rights Use data for legitimate research Maintain transparent information gathering Provide proper attribution Workflow Visualization [Daily Trigger] ⬇️ [Web Crawling] ⬇️ [Content Filtering] ⬇️ [AI Summarization] ⬇️ [Slack Delivery] Connect With Me Ready to revolutionize your market research? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your information gathering with intelligent, automated workflows! #AIResearch #MarketIntelligence #AutomatedInsights #TechTrends #WebCrawling #AIMarketing #InnovationTracking #BusinessIntelligence #DataAutomation #TechNews

Quickly transform any LinkedIn profile URL into a concise, AI‑generated professional summary — perfect for recruiters, sales teams, and hiring managers who need instant insights into prospects or candidates without manual research. How it works The workflow polls a Google Sheet for new or updated rows containing LinkedIn profile URLs. For each URL, the Real‑Time LinkedIn Scraper API (via RapidAPI) pulls experience and education sections. Extracted profile data is sent to OpenAI’s GPT model, which generates a clean, structured summary highlighting key strengths, career trajectory, and differentiators. The generated summary is written back into a new column in the same row of your Google Sheet for easy review and sharing. Set up steps Connect your Google account and select the spreadsheet + worksheet containing your list of LinkedIn URLs. Sign up for the Real‑Time LinkedIn Scraper API on RapidAPI, copy your API key, and add it to the workflow’s HTTP Request node. Insert your OpenAI API key credentials. Ensure your Google Sheet has one column for “linkedin_url” and create two empty columns named “full_name” and "summary" (or customize them based on your needs). Run a single row through the workflow to verify scraping accuracy and summary formatting, then turn on the workflow for continuous automation. With this template, eliminate hours of manual profile review — instantly gain actionable insights and focus on what really matters: building relationships and closing deals.

This n8n training workflow demonstrates how to connect a sub-workflow as a tool to an AI Agent. In this example, the main workflow is a Website Chatbot that engages visitors, collects contact information, and sends that data to a CRM process. The CRM process itself is a separate sub-workflow, connected to the agent as a tool via the Tool Workflow node. Step-by-Step Setup Instructions 1. Create the Sub-Workflow (CRM Tool) This sub-workflow will be triggered by the AI agent to process collected information. It will: Receive inputs (email, description) from the main chatbot workflow. Format the data into a structured JSON format. Append the data to a Google Sheet (acting as the CRM database). Send a confirmation message back to the main workflow. Steps inside the sub-workflow: When Executed by Another Workflow** – Triggered by the main workflow’s tool node. Convert Conversation (Agent)** – Uses OpenAI to extract and format the input into a JSON structure: { "email": "jane.doe@example.com", "description": "Wants help automating lead intake and sending Slack notifications." } Structured Output Parser – Ensures the extracted data matches the expected JSON schema. Append row in sheet (Google Sheets) – Adds the new lead data to your CRM sheet. Code Node – Returns a simple text confirmation like "Thanks for the info, we will be in touch soon". Required setup for Google Sheets: Enable the Google Sheets API and connect your Google account in n8n. Create a sheet with at least the columns email and description. Use the sheet's Document ID and tab name in the Google Sheets node. 2. Create the Main Workflow (Website Chatbot) This workflow acts as the main AI Agent handling incoming chat messages. Steps in the main workflow: When chat message received – Starts the workflow whenever a visitor sends a message via your chatbot integration. Website Chatbot (Agent Node) – Configured with a System Message that: Briefly explains your services. Asks the visitor what processes they want to automate. Requests their name and email. Sends collected data to the CRM tool once email and description are available. OpenAI Chat Model – Connects to the AI agent as its language model. Simple Memory – Stores short-term context for the ongoing chat. CRM Tool (Tool Workflow Node) – Points to the sub-workflow created in Step 1, allowing the chatbot to trigger it directly. 3. Connecting the Sub-Workflow to the AI Agent Add a Tool Workflow node to the main workflow. Select "Parameter" as the source. Paste in your sub-workflow JSON or select it from your n8n workflows. Connect the Tool Workflow node to your AI Agent using the ai_tool connection. Give the tool a clear description (e.g., crm tool to store lead information) so the agent knows when to use it. 4. How It Works in Action A visitor sends a message through the chatbot. The AI Agent engages, asks questions, and collects their name, email, and request. Once collected, the agent triggers the CRM Tool. The sub-workflow formats the data, stores it in Google Sheets, and sends a confirmation. The chatbot confirms with the visitor that their request was received. 5. Customization Ideas Replace Google Sheets with your actual CRM API. Add validation to ensure the email format is correct before saving. Expand the CRM tool to send a Slack or email notification after storing the lead. Created by Robert A. – Ynteractive Website: https://ynteractive.com Email: robert@ynteractive.com

This n8n template from Intuz provides a complete and automated solution for hyper-personalized email outreach. It powerfully combines AI with Gmail and Google Sheets, using specific keywords and prospect data to automatically craft unique, compelling email content that boosts engagement and secures more replies. Instead of manually replying to every lead or inquiry, this template does the heavy lifting for you, ensuring every response is relevant, thoughtful, and timely. It reads each person's unique inquiry, uses OpenAI to craft a perfectly tailored and human-like response, and sends it directly from your Gmail account. Ideal for sales, marketing, and customer support teams looking to boost engagement and save hours of manual work. Use Cases: Sales Teams: Instantly follow up with new leads from your website's contact form with a personalized touch. Customer Support: Provide initial, intelligent responses to support tickets, answering common questions or acknowledging receipt of a complex issue. Marketing Automation: Nurture leads by responding to content downloads or webinar sign-ups with relevant, non-generic information. Founders & Solopreneurs: Manage all incoming business inquiries (partnerships, media, etc.) efficiently without sacrificing quality. How It Works: Trigger the Flow (Manual): Start the automation whenever you're ready to process a new batch of inquiries from your sheet. Fetch Inquiries from Google Sheets: The workflow connects to your specified Google Sheet and reads each row. It pulls the contact's First Name, Email ID, the Inquiry Intent (e.g., "Demo Request," "Pricing Inquiry"), and the full text of their Original Inquiry. Sync Your Signature: Before writing the email, an HTTP Request node dynamically fetches your display name from your Gmail account settings. This ensures the signature in the generated email (Thanks, {{Your Name}}) is always accurate. Craft a Hyper-Personalized Reply with AI: It uses this context to generate a high-quality, professional, and friendly email reply in HTML format. For example: If the intent is "Technical Support," the AI will generate a helpful, empathetic response addressing the technical issue. If the intent is "Partnership Proposal," it will draft a professional reply acknowledging the proposal and outlining the next steps. Send via Gmail: The final node takes the AI-generated message, adds a relevant subject line (e.g., "Re: Your Demo Request"), and sends it directly to the contact's email address from your connected Gmail account. This process loops for every single row in your Google Sheet, turning a list of names into a series of meaningful conversations. Setup Instructions: To get this workflow running, you'll need to configure a few things: Credentials: Google: Connect your Google account via OAuth2 and ensure you have enabled access for Google Sheets, Google Drive, and Gmail. OpenAI: Add your OpenAI API key as a credential. Google Sheet Setup: Create a Google Sheet with the following exact column headers: -First Name -Email ID -Inquiry Intent (A short category like "Demo Request", "Billing Issue", etc.) -Original Inquiry (The full text of the email or message you received). Node Configuration: Get row(s) in sheet: Select your Google Sheet document and the specific sheet name. Message a model (OpenAI): Choose your preferred OpenAI model (e.g., gpt-4-turbo, gpt-3.5-turbo). HTTP Request & Send Personalized emails: These nodes should automatically use your configured Gmail credentials. No changes are typically needed. Connect with us Website: https://www.intuz.com/cloud/stack/n8n Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz

📚 Automated School Fee Reminder Workflow with Payment Link Automatically sends fee reminders (via email and WhatsApp) to parents with secure payment links, 3 days before the due date. 🔧 Main Components Daily Fee Check – 8 AM** Scheduled trigger that starts the workflow daily at 8 AM. Read Pending Fees** Fetches student fee records from an Excel sheet (using getAll method). Process Fee Reminders** Filters records to find pending fees due within the next 3 days. Prepare Email Reminder** Generates personalized email messages with payment links. Wait for Email Preparation** Adds delay/wait condition for email logic readiness. Send Email Reminder** Sends the fee reminder email with a secure payment link to the parent. Prepare WhatsApp Reminder** Generates WhatsApp-friendly messages with fee and payment details. Wait for WhatsApp Preparation** Waits for WhatsApp message logic to complete. Send WhatsApp Message** Sends the message to the parent’s WhatsApp number using a message API. Update Reminder Status** Updates the Excel file to mark reminders as sent to avoid duplicates. 🧩 Channels Used 📧 Email – with personalized payment link 💬 WhatsApp – formatted reminder message 🔐 Payment Integration Secure payment links are auto-generated per student to enable direct and safe online fee payments. ✅ Essential Prerequisites Excel sheet with fee records (student_fee_data.xlsx) SMTP credentials for sending email WhatsApp API or provider integration (like Twilio or Gupshup) Access to a payment gateway or service for link generation File storage access to update reminder status in Excel 📁 Required Excel File Structure (student_fee_data.xlsx) | Student ID | Name | Email | Phone | Fee Due Date | Amount | Reminder Sent | | ---------- | ---- | ----- | ----- | ------------ | ------ | ------------- | 🧾 Expected Input Format Example { "studentId": "ST123", "name": "Ria Mehta", "email": "ria.mehta@example.com", "phone": "+919123456789", "dueDate": "2025-08-10", "amount": "₹5000", "reminderSent": "No" } 🚀 Key Features ⏰ Scheduled Daily Execution – Fully automated at 8 AM 🧮 Due-Date Filtering – Only targets fees due in the next 3 days 💬 Multi-Channel Notifications – Sends reminders via both Email and WhatsApp 🔗 Secure Payment Links – Auto-generated for each student 🔄 Reminder Tracking – Prevents duplicate reminders by updating status ⚙️ Quick Setup Guide Import Workflow JSON into your n8n instance. Configure schedule in the “Daily Fee Check” node (default: 8 AM). Set Excel file path in the “Read Pending Fees” node. Update your fee processing logic in the “Process Fee Reminders” node. Add email credentials in the “Send Email Reminder” node. Integrate WhatsApp provider API in the “Send message” node. Define how you generate secure payment links. Test with sample data and activate workflow. 🛠️ Parameters to Configure | Parameter | Description | | ------------------ | ------------------------------------------ | | excel_file_path | Path to the fee tracking Excel file | | smtp_host | SMTP server for sending email reminders | | smtp_user | Email username | | smtp_password | Email password | | whatsapp_api_key | WhatsApp API key for sending messages | | payment_api_url | URL for generating payment links | | admin_email | (Optional) Admin email for error reporting |

📄 What this workflow does This workflow helps you analyze Google reviews of any business to generate powerful marketing insights. By simply submitting a business name and its Google Place ID, it fetches the top 30 reviews and uses GPT-4 (via LangChain Agent) to extract valuable customer insights such as marketing angles, customer motivations, product pain points, and voice of customer (VOC) quotes. The output is stored automatically in a connected Google Sheet. 👤 Who is this for Marketing teams looking for messaging inspiration Founders or product managers exploring customer feedback Brand strategists gathering real-world insights Agencies running VOC or sentiment analysis 🛠️ Requirements Dumpling AI API key** OpenAI GPT-4 or GPT-4o access** Google Sheets connection** A form or manual input with: Business Name Google Place ID ⚙️ How to set up Connect Credentials Dumpling AI (via HTTP Header Auth) OpenAI (GPT-4) Google Sheets (OAuth2) Prepare your Google Sheet Create columns: Business Name, Place ID, Marketing Angles, Customer Motivations, Frictions and Barriers, Product Opportunities, VOC Snippets Update Nodes Replace the Google Sheets Document ID and Tab Name with yours Check that the Dumpling API node is linked to your credential Optional: tweak the prompt in the LangChain Agent node to fit your tone or goals 🤖 How it works (Workflow Steps) User submits business name + Google Place ID Dumpling AI fetches top 30 reviews Workflow aggregates review text GPT-4 via LangChain analyzes the reviews Insights are parsed and logged to Google Sheets 💡 Customization Ideas Push output to Notion, Airtable, or Slack Add sentiment scoring to prioritize themes Create summaries for each insight category Schedule insights to be emailed weekly This is a plug-and-play VOC research workflow — great for founders, marketers, and product teams who want actionable data from real customers without doing manual review scraping or summarizing.