Building Your First WhatsApp Chatbot
Build your first WhatsApp chatbot with this comprehensive n8n workflow, designed to automate customer interactions and provide instant support. This powerful template connects WhatsApp for incoming messages, OpenAI for intelligent responses, and a vector store to manage product information, allowing you to create an AI sales agent that can answer questions and share product brochures. The workflow begins with a WhatsApp Trigger, which captures incoming messages and routes them through a "Handle Message Types" node to determine the appropriate response. For product-related inquiries, the AI Sales Agent, powered by OpenAI's Chat Model and supported by a Window Buffer Memory and Vector Store Tool, retrieves information from a "Product Catalogue" (an in-memory vector store populated by an "Extract from File" node after a "get Product Brochure" HTTP request) and crafts a personalized reply using the "Reply To User" WhatsApp node. This solution is ideal for businesses looking to enhance customer service, automate sales inquiries, and provide 24/7 support without human intervention, significantly reducing response times and operational costs.
Workflow JSON
{"meta": {"instanceId": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9"}, "nodes": [{"id": "77ee6494-4898-47dc-81d9-35daf6f0beea", "name": "WhatsApp Trigger", "type": "n8n-nodes-base.whatsAppTrigger", "position": [1360, -280], "webhookId": "aaa71f03-f7af-4d18-8d9a-0afb86f1b554", "parameters": {"updates": ["messages"]}, "credentials": {"whatsAppTriggerApi": {"id": "", "name": "[Your whatsAppTriggerApi]"}}, "typeVersion": 1}, {"id": "57210e27-1f89-465a-98cc-43f890a4bf58", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [1960, -200], "parameters": {"model": "gpt-4o-2024-08-06", "options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1}, {"id": "e1053235-0ade-4e36-9ad2-8b29c78fced8", "name": "Window Buffer Memory", "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow", "position": [2080, -200], "parameters": {"sessionKey": "=whatsapp-75-{{ $json.messages[0].from }}", "sessionIdType": "customKey"}, "typeVersion": 1.2}, {"id": "69f1b78b-7c93-4713-863a-27e04809996f", "name": "Vector Store Tool", "type": "@n8n/n8n-nodes-langchain.toolVectorStore", "position": [2200, -200], "parameters": {"name": "query_product_brochure", "description": "Call this tool to query the product brochure. Valid for the year 2024."}, "typeVersion": 1}, {"id": "170e8f7d-7e14-48dd-9f80-5352cc411fc1", "name": "Embeddings OpenAI", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [2200, 80], "parameters": {"model": "text-embedding-3-small", "options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1}, {"id": "ee78320b-d407-49e8-b4b8-417582a44709", "name": "OpenAI Chat Model1", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [2440, -60], "parameters": {"model": "gpt-4o-2024-08-06", "options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1}, {"id": "9dd89378-5acf-4ca6-8d84-e6e64254ed02", "name": "When clicking \u2018Test workflow\u2019", "type": "n8n-nodes-base.manualTrigger", "position": [0, -240], "parameters": {}, "typeVersion": 1}, {"id": "e68fc137-1bcb-43f0-b597-3ae07f380c15", "name": "Embeddings OpenAI1", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [760, -20], "parameters": {"model": "text-embedding-3-small", "options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1}, {"id": "2d31e92b-18d4-4f6b-8cdb-bed0056d50d7", "name": "Default Data Loader", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [900, -20], "parameters": {"options": {}, "jsonData": "={{ $('Extract from File').item.json.text }}", "jsonMode": "expressionData"}, "typeVersion": 1}, {"id": "ca0c015e-fba2-4dca-b0fe-bac66681725a", "name": "Recursive Character Text Splitter", "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter", "position": [900, 100], "parameters": {"options": {}, "chunkSize": 2000, "chunkOverlap": {}}, "typeVersion": 1}, {"id": "63abb6b2-b955-4e65-9c63-3211dca65613", "name": "Extract from File", "type": "n8n-nodes-base.extractFromFile", "position": [360, -240], "parameters": {"options": {}, "operation": "pdf"}, "typeVersion": 1}, {"id": "be2add9c-3670-4196-8c38-82742bf4f283", "name": "get Product Brochure", "type": "n8n-nodes-base.httpRequest", "position": [180, -240], "parameters": {"url": "https://usa.yamaha.com/files/download/brochure/1/1474881/Yamaha-Powered-Loudspeakers-brochure-2024-en-web.pdf", "options": {}}, "typeVersion": 4.2}, {"id": "1ae5a311-36d7-4454-ab14-6788d1331780", "name": "Reply To User", "type": "n8n-nodes-base.whatsApp", "position": [2820, -280], "parameters": {"textBody": "={{ $json.output }}", "operation": "send", "phoneNumberId": "477115632141067", "requestOptions": {}, "additionalFields": {"previewUrl": false}, "recipientPhoneNumber": "={{ $('WhatsApp Trigger').item.json.messages[0].from }}"}, "credentials": {"whatsAppApi": {"id": "", "name": "[Your whatsAppApi]"}}, "typeVersion": 1}, {"id": "b6efba81-18b0-4378-bb91-51f39ca57f3e", "name": "Reply To User1", "type": "n8n-nodes-base.whatsApp", "position": [1760, 80], "parameters": {"textBody": "=I'm unable to process non-text messages. Please send only text messages. Thanks!", "operation": "send", "phoneNumberId": "477115632141067", "requestOptions": {}, "additionalFields": {"previewUrl": false}, "recipientPhoneNumber": "={{ $('WhatsApp Trigger').item.json.messages[0].from }}"}, "credentials": {"whatsAppApi": {"id": "", "name": "[Your whatsAppApi]"}}, "typeVersion": 1}, {"id": "52decd86-ac6c-4d91-a938-86f93ec5f822", "name": "Product Catalogue", "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory", "position": [2200, -60], "parameters": {"memoryKey": "whatsapp-75"}, "typeVersion": 1}, {"id": "6dd5a652-2464-4ab8-8e5f-568529299523", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [-88.75, -473.4375], "parameters": {"color": 7, "width": 640.4375, "height": 434.6875, "content": "## 1. Download Product Brochure PDF\n[Read more about the HTTP Request Tool](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest)\n\nImport your marketing PDF document to build your vector store. This will be used as the knowledgebase by the Sales AI Agent.\n\nFor this demonstration, we'll use the HTTP request node to import the YAMAHA POWERED LOUDSPEAKERS 2024 brochure ([Source](https://usa.yamaha.com/files/download/brochure/1/1474881/Yamaha-Powered-Loudspeakers-brochure-2024-en-web.pdf)) and an Extract from File node to extract the text contents. "}, "typeVersion": 1}, {"id": "116663bc-d8d6-41a5-93dc-b219adbb2235", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [580, -476], "parameters": {"color": 7, "width": 614.6875, "height": 731.1875, "content": "## 2. Create Product Brochure Vector Store\n[Read more about the In-Memory Vector Store](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreinmemory/)\n\nVector stores are powerful databases which serve the purpose of matching a user's questions to relevant parts of a document. By creating a vector store of our product catalog, we'll allow users to query using natural language.\n\nTo keep things simple, we'll use the **In-memory Vector Store** which comes built-in to n8n and doesn't require a separate service. For production deployments, I'd recommend replacing the in-memory vector store with either [Qdrant](https://qdrant.tech) or [Pinecone](https://pinecone.io)."}, "typeVersion": 1}, {"id": "86bd5334-d735-4650-aeff-06230119d705", "name": "Create Product Catalogue", "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory", "position": [760, -200], "parameters": {"mode": "insert", "memoryKey": "whatsapp-75", "clearStore": true}, "typeVersion": 1}, {"id": "b8078b0d-cbd7-423f-bb30-13902988be38", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [1254, -552], "parameters": {"color": 7, "width": 546.6875, "height": 484.1875, "content": "## 3. Use the WhatsApp Trigger\n[Learn more about the WhatsApp Trigger](https://docs.n8n.io/integrations/builtin/trigger-nodes/n8n-nodes-base.whatsapptrigger/)\n\nThe WhatsApp Trigger allows you to receive incoming WhatsApp messages from customers. It requires a bit of setup so remember to follow the documentation carefully! Once ready however, it's quite easy to build powerful workflows which are easily accessible to users.\n\nNote that WhatsApp can send many message types such as audio and video so in this demonstration, we'll filter them out and just accept the text messages."}, "typeVersion": 1}, {"id": "5bf7ed07-282b-4198-aa90-3e5ae5180404", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [1640, 280], "parameters": {"width": 338, "height": 92, "content": "### Want to handle all message types?\nCheck out my other WhatsApp template in my creator page! https://n8n.io/creators/jimleuk/"}, "typeVersion": 1}, {"id": "a3661b59-25d2-446e-8462-32b4d692b69d", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [1640, -40], "parameters": {"color": 7, "width": 337.6875, "height": 311.1875, "content": "### 3a. Handle Unsupported Message Types\nFor non-text messages, we'll just reply with a simple message to inform the sender."}, "typeVersion": 1}, {"id": "ea3c9ee1-505a-40e7-82fe-9169bdbb80af", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [1840, -682.5], "parameters": {"color": 7, "width": 746.6875, "height": 929.1875, "content": "## 4. Sales AI Agent Responds To Customers\n[Learn more about using AI Agents](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/)\n\nn8n's AI agents are powerful nodes which make it incredibly easy to use state-of-the-art AI in your workflows. Not only do they have the ability to remember conversations per individual customer but also tap into resources such as our product catalogue vector store to pull factual information and data for every question.\n\nIn this demonstration, we use an AI agent which is directed to help the user navigate the product brochure. A Chat memory subnode is attached to identify and keep track of the customer session. A Vector store tool is added to allow the Agent to tap into the product catalogue knowledgebase we built earlier."}, "typeVersion": 1}, {"id": "5c72df8d-bca1-4634-b1ed-61ffec8bd103", "name": "Sticky Note6", "type": "n8n-nodes-base.stickyNote", "position": [2620, -560], "parameters": {"color": 7, "width": 495.4375, "height": 484.1875, "content": "## 5. Repond to WhatsApp User\n[Learn more about the WhatsApp Node](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.whatsapp/)\n\nThe WhatsApp node is the go-to if you want to interact with WhatsApp users. With this node, you can send text, images, audio and video messages as well as use your WhatsApp message templates.\n\nHere, we'll keep it simple by replying with a text message which is the output of the AI agent."}, "typeVersion": 1}, {"id": "48ec809f-ca0e-4052-b403-9ad7077b3fff", "name": "Sticky Note7", "type": "n8n-nodes-base.stickyNote", "position": [-520, -620], "parameters": {"width": 401.25, "height": 582.6283033962263, "content": "## Try It Out!\n\n### 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.\n\n* This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot.\n* A product brochure is imported via HTTP request node and its text contents extracted.\n* The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot.\n* A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out.\n* The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool.\n* The Agent's response is sent back to the user via the WhatsApp node.\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!"}, "typeVersion": 1}, {"id": "87cf9b41-66de-49a7-aeb0-c8809191b5a0", "name": "Handle Message Types", "type": "n8n-nodes-base.switch", "position": [1560, -280], "parameters": {"rules": {"values": [{"outputKey": "Supported", "conditions": {"options": {"version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict"}, "combinator": "and", "conditions": [{"operator": {"type": "string", "operation": "equals"}, "leftValue": "={{ $json.messages[0].type }}", "rightValue": "text"}]}, "renameOutput": true}, {"outputKey": "Not Supported", "conditions": {"options": {"version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict"}, "combinator": "and", "conditions": [{"id": "89971d8c-a386-4e77-8f6c-f491a8e84cb6", "operator": {"type": "string", "operation": "notEquals"}, "leftValue": "={{ $json.messages[0].type }}", "rightValue": "text"}]}, "renameOutput": true}]}, "options": {}}, "typeVersion": 3.2}, {"id": "e52f0a50-0c34-4c4a-b493-4c42ba112277", "name": "Sticky Note8", "type": "n8n-nodes-base.stickyNote", "position": [-80, -20], "parameters": {"color": 5, "width": 345.10906976744184, "height": 114.53583720930231, "content": "### You only have to run this part once!\nRun this step to populate our product catalogue vector. Run again if you want to update the vector store with a new version."}, "typeVersion": 1}, {"id": "c1a7d6d1-191e-4343-af9f-f2c9eb4ecf49", "name": "Sticky Note9", "type": "n8n-nodes-base.stickyNote", "position": [1260, -40], "parameters": {"color": 5, "width": 364.6293255813954, "height": 107.02804651162779, "content": "### Activate your workflow to use!\nTo start using the WhatsApp chatbot, you'll need to activate the workflow. If you are self-hosting ensure WhatsApp is able to connect to your server."}, "typeVersion": 1}, {"id": "a36524d0-22a6-48cc-93fe-b4571cec428a", "name": "AI Sales Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [1960, -400], "parameters": {"text": "={{ $json.messages[0].text.body }}", "options": {"systemMessage": "You are an assistant working for a company who sells Yamaha Powered Loudspeakers and helping the user navigate the product catalog for the year 2024. Your goal is not to facilitate a sale but if the user enquires, direct them to the appropriate website, url or contact information.\n\nDo your best to answer any questions factually. If you don't know the answer or unable to obtain the information from the datastore, then tell the user so."}, "promptType": "define"}, "typeVersion": 1.6}], "pinData": {}, "connections": {"AI Sales Agent": {"main": [[{"node": "Reply To User", "type": "main", "index": 0}]]}, "WhatsApp Trigger": {"main": [[{"node": "Handle Message Types", "type": "main", "index": 0}]]}, "Embeddings OpenAI": {"ai_embedding": [[{"node": "Product Catalogue", "type": "ai_embedding", "index": 0}]]}, "Extract from File": {"main": [[{"node": "Create Product Catalogue", "type": "main", "index": 0}]]}, "OpenAI Chat Model": {"ai_languageModel": [[{"node": "AI Sales Agent", "type": "ai_languageModel", "index": 0}]]}, "Product Catalogue": {"ai_vectorStore": [[{"node": "Vector Store Tool", "type": "ai_vectorStore", "index": 0}]]}, "Vector Store Tool": {"ai_tool": [[{"node": "AI Sales Agent", "type": "ai_tool", "index": 0}]]}, "Embeddings OpenAI1": {"ai_embedding": [[{"node": "Create Product Catalogue", "type": "ai_embedding", "index": 0}]]}, "OpenAI Chat Model1": {"ai_languageModel": [[{"node": "Vector Store Tool", "type": "ai_languageModel", "index": 0}]]}, "Default Data Loader": {"ai_document": [[{"node": "Create Product Catalogue", "type": "ai_document", "index": 0}]]}, "Handle Message Types": {"main": [[{"node": "AI Sales Agent", "type": "main", "index": 0}], [{"node": "Reply To User1", "type": "main", "index": 0}]]}, "Window Buffer Memory": {"ai_memory": [[{"node": "AI Sales Agent", "type": "ai_memory", "index": 0}]]}, "get Product Brochure": {"main": [[{"node": "Extract from File", "type": "main", "index": 0}]]}, "Recursive Character Text Splitter": {"ai_textSplitter": [[{"node": "Default Data Loader", "type": "ai_textSplitter", "index": 0}]]}, "When clicking \u2018Test workflow\u2019": {"main": [[{"node": "get Product Brochure", "type": "main", "index": 0}]]}}}How to Import This Workflow
- 1Copy the workflow JSON above using the Copy Workflow JSON button.
- 2Open your n8n instance and go to Workflows.
- 3Click Import from JSON and paste the copied workflow.
Don't have an n8n instance? Start your free trial at n8nautomation.cloud
Related Templates
Telegram AI multi-format chatbot
Automate a multi-format AI chatbot on Telegram, allowing users to interact via text or voice. This workflow connects Telegram for user input and replies with OpenAI for AI processing and voice-to-text conversion. It's ideal for customer support, content generation, or interactive learning platforms. This saves significant time and effort in managing diverse user interactions.
Agentic Telegram AI bot with LangChain nodes and new tools
Build an intelligent, agentic AI bot for Telegram that responds dynamically to user queries, leveraging the power of LangChain nodes and custom tools. This workflow connects Telegram as the primary communication channel, allowing the bot to listen for incoming messages via the Listen for incoming events trigger. User input is then processed by the AI Agent, which orchestrates interactions with an OpenAI Chat Model for natural language understanding and generation, and a Window Buffer Memory to maintain conversational context. The agent can dynamically decide to use a custom tool, Send back an image, which in turn calls the Generate image in Dalle tool (an AI:toolHttpRequest node) to create and send images based on user requests, before ultimately delivering the final reply back to the user via the Send final reply Telegram node. This setup is ideal for businesses offering interactive customer support, content creators generating on-demand visuals, or communities providing dynamic information retrieval, significantly reducing manual intervention and providing instant, intelligent responses to Telegram users. It automates complex conversational flows, saving significant time and resources typically spent on human-led interactions and content creation.
Discord AI bot
Automate the intelligent routing of incoming user requests from a webhook to the appropriate Discord department with this powerful workflow. This n8n workflow leverages OpenAI's advanced natural language processing to analyze user input, then directs the request to either the User Success Dept, IT Dept, or Helpdesk Discord channel based on the identified category. Imagine a customer submitting a query through a web form; this workflow instantly understands their need and posts it in the relevant team's Discord channel, eliminating manual triage and ensuring faster response times. This solution is ideal for businesses and support teams looking to streamline their customer service operations, reduce response delays, and improve internal communication by automatically categorizing and distributing inquiries, saving significant time and effort in managing support tickets.