Workflow Templates

📝 Description 🤖 What this workflow does This workflow turns Reddit pain points into emotionally-driven comic-style ads using AI. It takes in a product description, scrapes Reddit for real user pain points, filters relevant posts using AI, generates ad angles, rewrites them into 4-panel comic prompts, and finally uses Dumpling AI to generate comic-style images. All final creatives are uploaded to Google Drive. 🧠 What problem is this solving? Crafting ad content that truly speaks to customer struggles is time-consuming. This workflow automates that entire process — from pain point discovery to visual creative output — using AI and Reddit as a source of truth for customer language. 👤 Who is this for? Copywriters and performance marketers Startup founders and indie hackers Creatives building empathy-driven ad concepts Automation experts looking to generate scroll-stopping content ⚙️ Setup Instructions Here’s how to set everything up, step by step: 🔹 1. Trigger: Form Input Node: 📝 Form - Submit Product Info This form asks the user to enter: Brand Name Website Product Description ✅ Make sure this form is active and testable. 🔹 2. Generate Reddit Keyword Node: 🧠 GPT-4o - Generate Reddit Keyword Uses the product description to generate a search keyword based on what your audience might be discussing on Reddit. 🔹 3. Search Reddit Node: 🔍 Reddit - Search Posts Uses the keyword to search Reddit for relevant threads. Make sure your Reddit integration is properly configured. 🔹 4. Filter Valid Posts Node: 🔎 IF - Check Upvotes & Text Length Filters out low-effort or unpopular posts. Only keeps posts with: Minimum 2 upvotes Content at least 100 characters long ✅ You can adjust these thresholds in the node settings. 🔹 5. Clean Reddit Output Node: 🧼 Code - Structure Reddit Posts This formats the list of posts into clean JSON for the AI agents to process. 🔹 6. Check Relevance with AI Agent Node: 🤔 Langchain Agent - Post Relevance Classifier This node uses a LangChain agent (tool: think2) to determine if each post is relevant to your product. Only relevant ones are passed forward. 🔹 7. Aggregate Relevant Posts Node: 📦 Code - Merge Relevant Posts Collects all relevant posts into a clean format for the next GPT-4 call. 🔹 8. Generate Ad Angles Node: ✍️ GPT-4o - Generate Emotional Ad Angles Writes 10 pain-point-based marketing angles using real customer language. 🔹 9. Rank the Best Angles Node: 📊 GPT-4o - Rank Top 10 Angles Scores the generated angles and ranks them from most to least powerful. Only the top 3 are passed forward. 🔹 10. Turn Angles into Comic Prompts Node: 🎭 GPT-4o - Write Comic Scene Prompts Rewrites each of the top ad angles into a 4-panel comic strip structure (pain → tension → product → resolution). 🔹 11. Generate Comic Images Node: 🎨 Dumpling AI - Generate Comic Panels Sends each prompt to Dumpling AI to create visual comic scenes. 🔹 12. Wait for Image Generation Node: ⏳ Wait - Dumpling AI Response Time Adds a delay to give Dumpling AI time to finish generating all images. 🔹 13. Get Final Image URLs Node: 🔗 Code - Extract Image URLs from Dumpling Response Extracts all image links for preview/download. 🔹 14. Upload to Google Drive Node: ☁️ Google Drive - Upload Comics Uploads the comic images to your chosen Google Drive folder. ✅ Update this node with your destination folder ID. 🔹 15. Log Final Output Optional You can extend the flow to log the image links, ad angles, and Reddit sources to Google Sheets, Airtable, or Notion depending on your use case. 🛠️ How to Customize ✏️ Adjust tone: Update GPT-4 system prompts to sound more humorous, emotional, or brand-specific. 🧵 Use different styles: Swap Dumpling AI image settings for ink sketch, manga, or cartoon renderings. 🔄 Change input source: Replace Reddit with X (Twitter), Quora, or YouTube comments. 📦 Store results differently: Swap Google Drive for Notion, Dropbox, or Airtable. This workflow turns real audience struggles into thumb-stopping comic content — automatically.

🎥 YouTube Video AI Agent Workflow This n8n workflow template allows you to interact with an AI agent that extracts details and the transcript of a YouTube video using a provided video ID. Once the details and transcript are retrieved, you can chat with the AI agent to explore or analyze the video's content in a conversational and insightful manner. 🌟 How the Workflow Works 🔗 Input Video ID: The user provides a YouTube video ID as input to the workflow. 📄 Data Retrieval: The workflow fetches essential details about the video (e.g., title, description, upload date) and retrieves its transcript using YouTube's Data API and additional tools for transcript extraction. 🤖 AI Agent Interaction: The extracted details and transcript are processed by an AI-powered agent. Users can then ask questions or engage in a conversation with the agent about the video's content, such as: Summarizing the transcript. Analyzing key points. Clarifying specific sections. 💬 Dynamic Responses: The AI agent uses natural language processing (NLP) to generate contextual and accurate responses based on the video data, ensuring a smooth and intuitive interaction. 🚀 Use Cases 📊 Content Analysis**: Quickly analyze long YouTube videos by querying specific sections or extracting summaries. 📚 Research and Learning**: Gain insights from educational videos or tutorials without watching them entirely. ✍️ Content Creation**: Repurpose transcripts into blogs, social media posts, or other formats efficiently. ♿ Accessibility**: Provide an alternative, text-based way to interact with video content for users who prefer reading over watching. 🛠️ Resources for Getting Started Google Cloud Console** (for API setup): Visit Google Cloud's Get Started Guide to configure your API access. YouTube Data API Key Setup**: Follow this guide to create and manage your YouTube Data API key. Install n8n Locally**: Refer to this installation guide for setting up n8n on your local machine. ✨ Sample Prompts "Tell me about this YouTube video with id: JWfNLF_g_V0" "Can you provide a list of key takeaways from this video with id: [youtube-video-id]?"

Community Node Disclaimer: This workflow uses KlickTipp community nodes. How It Works AI Agent and KlickTipp Tools Integration via Telegram: This component connects a large language model (LLM), such as Claude or OpenAI, to the KlickTipp contact management platform through Telegram messaging. The AI Agent interprets natural language queries received from Telegram and dynamically maps them to KlickTipp API operations, enabling intuitive and automated contact handling through a familiar messaging interface. Key Features Telegram & LLM Interaction Setup: Captures messages received via Telegram bot as an alternative to the chat message node. Maintains conversation state using a memory buffer tied to Telegram chat IDs. Interprets user input using an LLM (Claude or OpenAI). Routes interpreted commands to specific KlickTipp tools based on detected intent. Sends responses back to Telegram users with operation results. KlickTipp Integration: Complete set of KlickTipp API endpoints included: Contact Management:** Add, update, get, list, delete, and unsubscribe contacts. Contact Tagging:** Tag, untag, list tagged contacts. Tag Operations:** Create, get, update, delete, list tags. Opt-In Processes:** List and retrieve opt-in process details. Data Fields:** List and get custom data fields. Redirects:** Retrieve redirect URLs. Use Cases Supported: Query contact information via email or name through Telegram messages. Identify and segment contacts by city, region, or behavior via Telegram commands. Create or update contacts from data provided in Telegram messages. Dynamically apply or remove tags to initiate campaigns through Telegram bot interactions. Automate targeted outreach based on contact attributes using Telegram as the control interface. Setup Instructions Install and Configure Nodes: Set up a Telegram bot using BotFather and obtain the bot token. Configure the Telegram Trigger node in n8n with your bot token. Configure the LLM model (e.g., OpenAI or Claude) and memory node if used. Connect all required KlickTipp nodes and authenticate using valid API credentials. Activate the workflow. Define Tagging and Field Mapping: Identify which fields and tags are relevant to your use cases. Ensure necessary tags and custom fields are already created in KlickTipp. Workflow Logic: Trigger via Telegram: A message is received by the Telegram bot and passed to the AI Agent. Query Handling via LLM Agent: AI interprets the natural language input and determines the action. Contact Search & Segmentation: Searches contacts using identifiers (email, address) or criteria. Data Operations: Retrieves, updates, or manages contact and tag data based on interpreted command. Campaign Preparation: Applies tags or sends campaign triggers depending on query results. Response via Telegram: Sends formatted results back to the Telegram user. Benefits: Mobile-First Interface:** Users can manage KlickTipp contacts directly from Telegram on any device. AI-Powered Automation:** Reduces manual contact search and tagging efforts through intelligent processing. Scalable Integration:** Built-in support for full range of KlickTipp operations allows diverse use-case handling. Data Consistency:** Ensures structured data flows between Telegram, AI, and KlickTipp, minimizing errors. Testing and Deployment: Use defined Telegram messages such as: “Tell me something about the contact with email address X” “Tag all contacts from region Y” “Send campaign Z to customers in area A” Validate expected actions in KlickTipp after message execution and confirm responses in Telegram. Notes: Customization:** Adjust tag logic, AI prompts, and contact field mappings based on project needs. Extensibility:** The template can be expanded with further logic for Google Sheets input or campaign feedback loops Resources: Use KlickTipp Community Node in n8n Automate Workflows: KlickTipp Integration in n8n

Effortlessly track your expenses with MoneyMate, an n8n workflow that transforms receipts into organized financial insights. Upload a photo or text via Telegram, and let MoneyMate extract key details—store info, transaction dates, items, and totals—using Google Vision OCR and AI-powered parsing via OpenRouter. It categorizes expenses (e.g., Food & Beverages, Transport, Household) and delivers a clean, emoji-rich summary back to your Telegram chat. Handles zero-total errors with a friendly nudge to double-check inputs. Perfect for freelancers, small business owners, or anyone seeking hassle-free expense management. No database required, ensuring privacy and simplicity. Deploy MoneyMate and take control of your finances today! Key Features 📱 Telegram Integration: Input via photo or text, receive summaries instantly. 📸 Receipt Scanning: Converts receipt images to text using Google Vision API. 🤖 AI Parsing: Categorizes transactions with OpenRouter’s AI analysis. 🛡️ Privacy-First: Processes data on-the-fly without storage. ⚠️ Smart Error Handling: Catches zero totals with user-friendly prompts. 📊 Flexible Categories: Supports Income/Expense and custom expense types. Ideal For Budget-conscious individuals** managing personal finances. Entrepreneurs** tracking business expenses. Teams** needing quick, automated expense reporting. Pre-Requirements n8n Instance:** A running n8n instance (cloud or self-hosted). Credentials:** Telegram: A bot token and webhook setup (obtained via BotFather). For more information, please refer to Telegram bots creation Google Cloud: A service account with Google Vision API enabled and API key. For more informations, please refer to Google cloud Vision OpenRouter: An account with API access for AI language model usage. Telegram Bot:* A configured *Telegram** bot to receive inputs and send summaries. Setup Instructions Import Workflow:* Copy the *MoneyMate** workflow JSON and import it into your n8n instance using the "Import Workflow" option. Set Up Telegram Bot:* Create a bot via BotFather on *Telegram** to get a token and set up a webhook. For detailed steps, refer to n8n’s Telegram setup guide. Configure Credentials:** In the Telegram Trigger, Send Error Message, and Send Expense Summary nodes, add Telegram API credentials with your bot token. In the Get Telegram File and Download Image nodes, ensure Telegram API credentials are linked. In the Google Vision OCR node, add Google Cloud credentials with Google Vision API access. In the OpenRouter AI Model node, set up OpenRouter API credentials. Test the Workflow:* Send a test receipt photo or text (e.g., "Lunch 50,000 IDR") via *Telegram** and verify the summary in your chat. Activate:** Enable the workflow in n8n to run automatically for each input. Customization Options Add Categories:* Modify the *AI Categorizer* node to include new expense types (e.g., *Entertainment**). Change Output Format:* Adjust the *Format Summary Message** node to include more details like taxes or payment methods. Switch AI Model:* In the *OpenRouter AI Model* node, select a different *OpenRouter** model for better parsing. Store Data:* Add a *Google Sheets* node after *Parse Receipt Data** to save expense records. Enhance Errors:* Include an email notification node after *Check Invalid Input** for failed inputs. Why Choose MoneyMate? Save time, reduce manual entry, and gain clarity on your spending with MoneyMate’s AI-driven workflow. Ready to streamline your finances? Get MoneyMate now! Made by: khmuhtadin Need a custom? contact me on LinkedIn or Web

Automated Financial Reporting Using Google Vision OCR, Telegram & Google Sheets This workflow automates the process of recording financial transactions from photos of receipts or shopping receipts. Users simply send an image of the receipt via Telegram. The image is processed using the Google Vision API to detect text, then extracted and structured by LLM via OpenRouter. The final result is saved to Google Sheets and also displayed to the user via a Telegram bot. 🧾 Google Sheets Template Create a Google Sheet using this template: Financial Reporting 🛠️ Key Features The workflow starts when a user sends a photo of a receipt to the Telegram bot. The image is converted to text using the Google Vision API's OCR. Data processing with LLM (OpenRouter) helps identify and structure transaction elements such as: date, vendor name & address, receipt/invoice number, item list (product name, quantity, unit price, total), and transaction category. Cleaned and structured data is automatically recorded to Google Sheets per item. The system also sends a summary of the recording results in an easy to read text format. Users can also send text messages to the bot to query stored transaction data, which will be answered by a Google Sheets-based AI Agent. 🔧 Requirements Active Telegram Bot + API Token Google Vision API Key OpenRouter Account + API Key Google Sheets connected to n8n 🧩 Setup Instructions Replace all API keys and tokens with your own in the relevant nodes. Google Vision API Key: Set in 'Set Vision API' node. Telegram Bot Token: Set in 'Set Telegram Token' node and all Telegram nodes. OpenRouter API Key: Set in all OpenRouter nodes. Google Sheets: Connect your own Google Sheets credential. Use the provided Google Sheets template or your own. Activate the workflow after configuration. (Optional) Review sticky notes for step-by-step explanations.

How it works This template is a hands-on, practical exam designed to test your understanding of the fundamental JSON data types. It's the perfect way to solidify your knowledge after learning the basics. Think of it as the "driver's test" that comes after the "theory lesson". You'll be given a series of tasks, and the workflow will automatically check your answers, providing instant feedback. The test is broken down into six sequential challenges, each focusing on a core data type: String: Writing text values correctly. Number: Using integers and decimals. Boolean: Working with true and false. Null: Representing a non-existant value. Array: Creating ordered lists of data. Object: Building nested key-value structures. For each challenge, you'll modify a Set node with the correct JSON syntax. When you execute the workflow, a corresponding IF node will validate your input. A green path means you passed and can move to the next challenge. A red path means you need to try again! Set up steps Setup time: < 1 minute This workflow is a self-contained test and requires no setup or credentials. Read the instructions on the main sticky note to understand the goal. Start with the first challenge, "Test - String". Activate and modify the node according to the instructions on the purple sticky note next to it. Click "Execute Workflow". If the execution path is green, you've passed! You can move on to the next "Test" node in the sequence to continue. If the path is red, read the hint in the error message and try again. Repeat the process until you reach the final success message. Good luck!

How It Works This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, the official n8n documentation—and then build a chatbot to ask it questions. Think of it like this: instead of a general-knowledge AI, you're building an expert librarian. 🔧 Workflow Overview The workflow is split into two main parts: Part 1: Indexing the Knowledge (📚 Building the Library) This is a one-time process you run manually. The workflow will: Automatically scrape all pages of the n8n documentation. Break them down into small, digestible chunks. Use an AI model to create a numerical representation (an embedding) for each chunk. Store these embeddings in n8n's built-in Simple Vector Store. > This is like a librarian reading every book and creating a hyper-detailed index card for every paragraph. > ⚠️ Important: This in-memory knowledge base is temporary. It will be erased if you restart your n8n instance. You'll need to run the indexing process again in that case. Part 2: The AI Agent (🧠 The Expert Librarian) This is the chat interface. When you ask a question: The AI agent doesn't guess the answer. It searches the knowledge base to find the most relevant “index cards” (chunks). It feeds those chunks to a language model (Gemini) with strict instructions: > “Answer the user's question using ONLY this information.” This ensures answers are accurate, factual, and grounded in your documents. 🚀 Setup Steps > Total setup time: ~2 minutes > Indexing time: ~15–20 minutes This template uses n8n’s built-in tools, so no external database is needed. 1. Configure OpenAI Credentials You’ll need an OpenAI API key (for GPT models). In your n8n workflow: Go to any of the three OpenAI nodes (e.g., OpenAI Chat Model). Click the Credential dropdown → + Create New Credential. Enter your OpenAI API key and save. 2. Apply Credentials to All Nodes Your new credential is now saved. Go to the other two OpenAI nodes (e.g., OpenAI Embeddings) and select the newly created credential from the dropdown. 3. Build the Knowledge Base Find the Start Indexing manual trigger node (top-left of the workflow). Click the Execute Workflow button to start indexing. > ⚠️ Be patient: This takes 15–20 minutes to scrape and process the full documentation. > You only need to do this once per n8n session. 4. Chat With Your Expert Agent After indexing completes, activate the entire workflow (toggle at the top). Open the RAG Chatbot chat trigger node (bottom-left). Copy its Public URL. Open it in a new tab and ask questions about n8n! Example questions: "How does the IF node work?" "What is a sub-workflow?" 👤 Credits All credits go to Lucas Peyrin 🔗 lucaspeyrin on n8n.io

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This comprehensive RAG workflow enables your AI agents to answer user questions with contextual knowledge pulled from your own documents — using metadata-rich embeddings stored in Supabase. 🔧 Key Features: RAG Agents powered by GPT-4.5 or GPT-3.5 via OpenRouter or OpenAI. Supabase Vector Store to store and retrieve document embeddings. Cohere Reranker to improve response relevance and quality. Metadata Agent to enrich vectorized data before ingestion. PDF Extraction Flow to automatically parse and upload documents with metadata. ✅ Setup Steps: Connect your Supabase Vector Store. Use OpenAI Embeddings (e.g. text-embedding-3-small). Add API keys for OpenAI and/or OpenRouter. Connect a reranker like Cohere. Process documents with metadata before embedding. Start chatting — your AI agent now returns context-rich answers from your own knowledge base! Perfect for building AI assistants that can reason, search and answer based on internal company data, academic papers, support docs, or personal notes.

👔 Who is this for? Entrepreneurs and startup founders preparing for investors Business consultants drafting complete client plans Strategy teams building long-term business models Accelerators, incubators, or pitch trainers ❓ What problem does this workflow solve? Writing a full business plan takes days of work, multiple tools, and often gets stuck in messy docs or slides. This template automates every major section, generating a clean, detailed, and professional business plan with AI in just minutes. ⚙️ What this workflow does Starts with a chat message asking for your business idea or startup concept Passes the idea through 83 intelligent agents, each handling a full business plan chapter: Executive Summary Problem & Solution Product Description Market Research Competitor Analysis Business Model Marketing Strategy (includes guerrilla ideas) Operational Plan Financial Plan Team & Advisors Roadmap Conclusion & Next Steps Each section uses tailored prompts and business logic Combines all outputs into a structured, professional Markdown file Final result: a ready-to-export business plan in seconds 🛠️ Setup Import this template into your n8n instance Replace the “LLM Chat Model” node with your preferred model (Ollama, GPT-4, etc.) Start from the chat input node — describe your startup or idea Wait for all agents to finish Download the final Business plan file 🤖 LLM Flexibility (Choose Your Model) Supports: OpenAI (GPT-4 / GPT-3.5) Ollama (LLaMA 3.1, Mistral, DeepSeek, etc.) Any compatible N8N chat model To change the model, just replace the “Language Model” node — no other updates required 📌 Notes All nodes are clearly named by function (e.g., “Market Research Generator”) Sticky notes included for clarity Generates high-quality plans suitable for VCs or accelerators Modular: you can turn off or reorder any chapter 📩 Need help? Email: sinamirshafiee@gmail.com Happy to support setup, LLM switching, or custom section development.

This workflow is a highly advanced multimodal AI assistant designed to operate through WhatsApp. It can understand and respond to text, images, voice messages, and PDF documents by combining OpenAI models with smart logic to adapt to the content received. 🎯 Core Features 📥 1. Automatic Message Type Detection Using the Input type node, the bot detects whether the user has sent: Text Voice messages Images Files (PDF) Other unsupported content 💬 2. Smart Text Message Handling Text messages are processed by an OpenAI GPT-4o-mini agent with a customized system prompt. Replies are concise, accurate, and formatted for mobile readability. 🖼️ 3. Image Analysis & Description Images are downloaded, converted to base64, and analyzed by an image-aware AI model. The output is a rich, structured description, designed for visually impaired users or visual content interpretation. 🎙️ 4. Voice Message Transcription & Reply Audio messages are downloaded and transcribed using OpenAI Whisper. The transcribed text is analyzed and answered by the AI. Optionally, the AI reply can be converted back to voice using OpenAI's text-to-speech, and sent as an audio message. 📄 5. PDF Document Extraction & Summary Only PDFs are allowed (filtered via MIME type). The document’s content is extracted and combined with the user's message. The AI then provides a relevant summary or answer. 🧠 6. Contextual Memory Each user has a personalized session ID with a memory window of 10 interactions. This ensures a more natural and contextual conversation flow. How It Works Thisworkflow is designed to handle incoming WhatsApp messages and process different types of inputs (text, audio, images, and PDF documents) using AI-powered analysis. Here’s how it functions: Trigger: The workflow starts with the **WhatsApp Trigger node, which listens for incoming messages (text, audio, images, or documents). Input Routing: The **Input type (Switch node) checks the message type and routes it to the appropriate processing branch: Text: Directly forwards the message to the AI agent for response generation. Audio: Downloads the audio file, transcribes it using OpenAI, and sends the transcription to the AI agent. Image: Downloads the image, analyzes it with OpenAI’s GPT-4 model, and generates a detailed description. PDF Document: Downloads the file, extracts text, and processes it with the AI agent. Unsupported Formats: Sends an error message if the input is not supported. AI Processing: The **AI Agent1 node, powered by OpenAI, processes the input (text, transcribed audio, image description, or PDF content) and generates a response. Response Handling**: For audio inputs, the AI’s response is converted back into speech (using OpenAI’s TTS) and sent as a voice message. For other inputs, the response is sent as a text message via WhatsApp. Memory: The **Simple Memory node maintains conversation context for follow-up interactions. Setup Steps To deploy this workflow in n8n, follow these steps: Configure WhatsApp API Credentials: Set up WhatsApp Business API credentials (Meta Developer Account). Add the credentials in the WhatsApp Trigger, Get Image/Audio/File URL, and Send Message nodes. Set Up OpenAI Integration: Provide an OpenAI API key in the Analyze Image, Transcribe Audio, Generate Audio Response, and AI Agent1 nodes. Adjust Input Handling (Optional): Modify the Switch node ("Input type") to handle additional message types if needed. Update the "Only PDF File" IF node to support other document formats. Test & Deploy: Activate the workflow and test with different message types (text, audio, image, PDF). Ensure responses are correctly generated and sent back via WhatsApp. Need help customizing? Contact me for consulting and support or add me on Linkedin.

🎧 Daily RSS Digest & Podcast Generation This workflow automates the creation of a daily sports podcast from your favorite news sources. It fetches articles, uses AI to write a digest and a two-person dialogue, and produces a single, merged audio file with KOKORO TTS ready for listening. ✨ How it works: 📰 Fetch & Filter Daily News: The workflow triggers daily, fetches articles from your chosen RSS feeds, and filters them to keep only the most recent content. ✍️ Generate AI Digest & Script: Using Google Gemini, it first creates a written summary of the day's news. A second AI agent then transforms this news into an engaging, conversational podcast script between two distinct AI speakers. 🗣️ Generate Voices in Chunks: The script is split into individual lines of dialogue. The workflow then loops through each line, calling a Text-to-Speech (TTS) API to generate a separate audio file (an MP3 chunk) for each part of the conversation. 🎛️ Merge Audio with FFmpeg: After all the audio chunks are created and saved locally, a command-line script generates a list of all the files and uses FFmpeg to losslessly merge them into a single, seamless MP3 file. All temporary files are then deleted. 📤 Send the Final Podcast: The final, merged MP3 is read from the server and delivered directly to your Telegram chat with a dynamic, dated filename. You can modify: 📰 The RSS Feeds to any news source you want. 🤖 The AI Prompts to change the tone, language, or style of the digest and podcast. 🎙️ The TTS Voices used for the two speakers. 📫 The Final Delivery Method (e.g., send to Discord, save to Google Drive, etc.). Perfect for creating a personalized, hands-free news briefing to listen to on your commute. Inspired by: https://n8n.io/workflows/6523-convert-newsletters-into-ai-podcasts-with-gpt-4o-mini-and-elevenlabs/

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.