Block 1 - WhatsApp Trigger
- Type / Role
- n8n-nodes-base.whatsAppTrigger - whatsAppTrigger
- Config choices
- Version 1
This workflow is provided as-is. Please review and test before using in production.
The " WhatsApp Productivity Assistant with Memory and AI Imaging " is a comprehensive n8n workflow that transforms your WhatsApp into a powerful, multi talented AI assistan...
n8n-nodes-base.whatsapptrigger, @n8n/n8n-nodes-langchain.agent, n8n-nodes-base.httprequest, n8n-nodes-base.openweathermaptool, n8n-nodes-base.whatsapp, @n8n/n8n-nodes-langchain.googlegemini, n8n-nodes-base.switch, @n8n/n8n-nodes-langchain.toolthink
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Iniyavan JC.
Original n8n.io sourceThe "WhatsApp Productivity Assistant with Memory and AI Imaging" is a comprehensive n8n workflow that transforms your WhatsApp into a powerful, multi-talented AI assistant. It's designed to handle a wide range of tasks by understanding user messages, analyzing images, and connecting to various external tools and services. The assistant can hold natural conversations, remember past interactions using a MongoDB vector store (RAG), and decide which tool is best suited for a user's request. Whether you need to check your schedule, research a topic, get the latest news, create an image, or even analyze a picture you send, this workflow orchestrates it all seamlessly through a single WhatsApp chat interface.
The workflow is structured into several interconnected components:
Route Message by Type (Image/Text) node then intelligently routes the message based on its content type. A Typing.... node sends a typing indicator to the user for a better experience. If an image is received, it's downloaded, processed via an HTTP Request, and analyzed by the Analyze image node. The Code1 node then standardizes both text and image analysis output into a single, unified input for the main AI agent.AI Agent1 node receives the user's input, maintains short-term conversational memory using Simple Memory, and uses a powerful language model (gpt-oss-120b2 or gpt-oss-120b1) to decide which tool or sub-agent to use. It orchestrates all the other agents and tools.Google Calendar, Google Tasks, and Gmail, allowing you to schedule events, manage to-dos, and read emails. It leverages a language model (gpt-4.1-mini or gemini-2.5-flash) for understanding and executing commands within these tools.Brave Web Search, Brave News Search, Wikipedia, Tavily, and a custom perprlexcia search) to find the most accurate and up-to-date information from the web. It uses a language model (gpt-oss-120b or gpt-4.1-nanoChat Model1) for reasoning.Webhook2) that processes conversation history, extracts key information using Extract Memory Info, and stores it in a MongoDB Atlas Vector Store for long-term memory. This allows the AI agent to remember past preferences and facts.Webhook3) triggered when a user asks to create an image. It uses a dedicated AI Agent with MongoDB Atlas Vector Store1 for contextual image prompt generation, Clean Prompt Text1 to refine the prompt, an HTTP Request to an external image generation API (e.g., Together.xyz), and then converts and sends the generated image back to the user via WhatsApp.Before importing and running this template, you will need:
gpt-oss-120b, gpt-5-nano, gpt-4o-mini).codestral-embed-2505).perprlexcia tool (the current URL http://self hoseted perplexcia/api/search implies a self-hosted or custom endpoint).perprlexcia) must be publicly accessible.You will need to set up the following credentials within your n8n instance:
WhatsApp Trigger node.Send message2, Send message3, Download media, and Typing.... nodes.Analyze image, Google Gemini Chat Model, gemini-2.5-flash, and Google Gemini Chat Model5 nodes.Get Weather Forecast node.gpt-oss-120b node.MongoDB Atlas Vector Store nodes.gpt-5-nano and gpt-4.1-nanoChat Model1 nodes.Get many messages and Get a message nodes (ensure correct Gmail OAuth2 setup for each).HTTP Request5 (used in media download).Brave Web Search and Brave News Search nodes.gpt-4.1-mini, gpt-oss-120b, gpt-oss-120b2, and gpt-4.1-nano nodes.Tavily web search (create a new one named "Tavily API Key" with Authorization: Bearer YOUR_TAVILY_API_KEY) and HTTP Request (for Together.xyz, e.g., "Together.xyz API Key").codestral-embed-2505, codestral-embed-, and codestral-embed-2506 nodes.This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 71 workflow blocks. Download the JSON for the full node graph.
| Workflow | Build a WhatsApp assistant with memory, Google Suite & multi-AI research and imaging |
|---|---|
| Complexity | advanced |
| Nodes | 71 |
| Categories | AI Chatbot, Multimodal AI |
| Author | Iniyavan JC |
| Published | 20 Aug 2025 |
Use the JSON export at /data/workflows/7663/7663.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
The " WhatsApp Productivity Assistant with Memory and AI Imaging " is a comprehensive n8n workflow that transforms your WhatsApp into a powerful, multi talented AI assistan...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your AI Chatbot, Multimodal AI use case.