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Build a WhatsApp assistant for text, audio & images using GPT-4o & Evolution API

Antonio GassoAntonio Gasso
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2/3/2026
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Build an intelligent WhatsApp assistant that automatically responds to customer messages using AI. This template uses the Evolution API community node for WhatsApp integration and OpenAI for natural language processing, with built-in conversation memory powered by Redis to maintain context across messages.

> ⚠️ Self-hosted requirement: This workflow uses the Evolution API community node, which is only available on self-hosted n8n instances. It will not work on n8n Cloud.

What this workflow does

  1. Receives incoming WhatsApp messages via Evolution API webhook
  2. Filters and processes text, audio, and image messages
  3. Transcribes audio messages using OpenAI Whisper
  4. Analyzes images using GPT-4 Vision
  5. Generates contextual responses with conversation memory
  6. Sends replies back through WhatsApp

Who is this for?

  • Businesses wanting to automate customer support on WhatsApp
  • Teams needing 24/7 automated responses with AI
  • Developers building multimodal chat assistants
  • Companies looking to reduce response time on WhatsApp

Setup instructions

  1. Evolution API: Install and configure Evolution API on your server. Create an instance and obtain your API key and instance name.
  2. Redis: Set up a Redis instance for conversation memory. You can use a local installation or a cloud service like Redis Cloud.
  3. OpenAI: Get your API key from platform.openai.com with access to GPT and Whisper models.
  4. Webhook: Configure your Evolution API instance to send webhooks to your n8n webhook URL.

Customization options

  • Modify the system prompt in the AI node to change the assistant's personality and responses
  • Adjust the Redis TTL to control how long conversation history is retained
  • Add additional message type handlers for documents, locations, or contacts
  • Integrate with your CRM or database to personalize responses

Credentials required

  • Evolution API credentials (self-hosted)
  • OpenAI API key
  • Redis connection

n8n WhatsApp Assistant with GPT-4o and Evolution API

This n8n workflow enables you to build a sophisticated WhatsApp assistant that can process text, audio, and image messages using OpenAI's GPT-4o model and the Evolution API for WhatsApp. It leverages LangChain for conversational AI capabilities, including memory management with PostgreSQL.

What it does

This workflow automates the following steps:

  1. Receives WhatsApp Messages: Listens for incoming messages via a Webhook, acting as the entry point for all WhatsApp interactions.
  2. Initial Message Processing: Sets up initial message data for further processing.
  3. Determines Message Type: Uses a Switch node to intelligently route messages based on whether they contain text, audio, or images.
  4. Handles Text Messages:
    • Retrieves chat history from a PostgreSQL database using LangChain's Postgres Chat Memory.
    • Processes the text message using an OpenAI Chat Model (GPT-4o) within a Basic LLM Chain.
    • Updates the chat history in PostgreSQL.
    • Sends the AI-generated text response back to WhatsApp via the Evolution API.
  5. Handles Audio Messages:
    • Converts the incoming audio file to a suitable format if necessary.
    • Transcribes the audio to text using OpenAI's speech-to-text capabilities (Whisper) via the OpenAI node.
    • Retrieves chat history from Postgres Chat Memory.
    • Processes the transcribed text using an OpenAI Chat Model (GPT-4o) within a Basic LLM Chain.
    • Updates the chat history in PostgreSQL.
    • Sends the AI-generated text response back to WhatsApp via the Evolution API.
  6. Handles Image Messages:
    • Converts the incoming image file to a suitable format if necessary.
    • Analyzes the image using OpenAI's vision capabilities via the OpenAI node.
    • Retrieves chat history from Postgres Chat Memory.
    • Processes the image analysis result using an OpenAI Chat Model (GPT-4o) within a Basic LLM Chain.
    • Updates the chat history in PostgreSQL.
    • Sends the AI-generated text response back to WhatsApp via the Evolution API.
  7. Manages Conversational Context: Utilizes Postgres Chat Memory to maintain conversation history, allowing the AI to remember previous interactions within the same chat.
  8. Error Handling/Fallback: Includes a No Operation node as a placeholder for messages that don't match the defined types or for potential future extensions.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Evolution API Account: An account with Evolution API to send and receive WhatsApp messages. You will need your API key and instance URL.
  • OpenAI API Key: An API key for OpenAI to access GPT-4o for text, audio transcription (Whisper), and image analysis capabilities.
  • PostgreSQL Database: Access to a PostgreSQL database for storing chat memory.
  • LangChain Integration: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.
  • Redis (Optional but Recommended): A Redis instance for caching or other temporary data storage, though its specific use in this JSON is minimal, it's present in the definition.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Evolution API: Create an Evolution API credential with your API key and instance URL.
    • OpenAI: Create an OpenAI credential with your API key.
    • PostgreSQL: Configure a PostgreSQL credential pointing to your database.
    • Redis: If used, configure a Redis credential.
  3. Webhook Setup:
    • Activate the Webhook node and copy its URL.
    • Configure your Evolution API instance to send incoming WhatsApp messages to this n8n Webhook URL.
  4. Customize AI Logic:
    • Review the OpenAI Chat Model and AI Agent nodes. Adjust the prompts and model parameters as needed to refine your assistant's behavior.
    • The Structured Output Parser node suggests that the AI is expected to return data in a specific format, which you might need to define or adjust based on your needs.
  5. Database Schema: Ensure your PostgreSQL database has a table configured for Postgres Chat Memory to store chat history. The specific schema will depend on how LangChain's memory module is configured.
  6. Activate the Workflow: Once all credentials are set up and configurations are complete, activate the workflow.

Your WhatsApp assistant will now be ready to process messages, engage in conversations, and respond intelligently based on text, audio, and image inputs.

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