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WhatsApp customer support bot with GPT-4 Mini, Google Sheets & Rapiwa API

RapiwaRapiwa
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2/3/2026
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Who Is This For?

This n8n automation workflow is designed for customer support teams, business owners, or service providers who want to automate customer interactions on WhatsApp.

If you regularly receive customer queries about your products, services, or technical issues — and need a system that can instantly respond, fetch data from Google Sheets or Docs, log support tickets, and send human-like replies — this workflow is for you. It’s perfect for teams using Rapiwa, Google Sheets, and Google Docs who want to provide a smart, AI-driven, yet personal support experience.

What This Workflow Does

This workflow is structured around a single intelligent AI assistant called Rapiwa that interacts with customers in real time through WhatsApp.

Key Features

AI-Driven Support Assistant (Rapiwa)
WhatsApp Integration via Rapiwa API
Dynamic Data Access (Google Sheets + Docs)
Knowledge Base Search
Conversation Memory
Automatic Logging
Multi-Product Support

Workflow Overview

  1. Rapiwa Trigger (Start Node)

    • Starts the workflow automatically whenever a new WhatsApp message is received in your Rapiwa account.
    • Example: When a customer sends a message like “What’s the price of SocialVibe?” or “I can’t access my dashboard”, this node triggers the workflow.

  2. If (Check Text)

    • Detects if the incoming message contains text (not just images, videos, or audio).
    • If it’s text, the workflow continues; otherwise, it stops or handles it differently.

  3. AI Agent – Customer Support Agent

    • This is the brain of the system — your AI Assistant (Rapiwa).
    • Interprets the user’s question, retrieves information, and replies in a clear, WhatsApp-friendly format.
    • Reads product details and company info from Google Sheets/Docs.
    • Fetches documentation links from the connected “Support Desk” and product-specific HTTP tools.
    • Logs customer issues to the support sheet for tracking and analysis.

  4. Memory (Session Context)

    • Stores chat history per user session so Rapiwa remembers context during a conversation.

  5. Research (AI Support Tool)

    • Acts as Rapiwa’s research assistant — gathers and organizes information from multiple sources.
    • Sources: Google Sheets, Google Docs, HTTP Tools, and Support Desk.

  6. Replay (Rapiwa Send Message)

    • Sends the AI’s final message back to the customer on WhatsApp using the Rapiwa API.
    • WhatsApp-optimized plain text messages only.

Data & Integrations

🔹 Google Sheets (Database)

  • Product Data Sheet: Holds product names, descriptions, and pricing.
  • Service Data Sheet: Lists offered services with details.
  • Support Log Sheet: Records each issue (Issue, Category, Solution).

🔹 Google Docs

  • Provides company information when a user asks about your organization.

Example Use Case

User Message:
> “Hi, I’m having a problem with my Faculty login.”

Rapiwa’s AI Response:
> “I’m sorry you’re having trouble logging in to Faculty. Please try resetting your password here: https://faculty.spagreen.net/docs/#reset-password
> If the issue continues, I can log this for support. Would you like me to do that?”

Useful Links

Support & Help

n8n AI Agent Workflow

This n8n workflow demonstrates a basic setup for an AI Agent using Langchain nodes, featuring a chat model, simple memory, and a 'Think' tool. It provides a foundational structure for building conversational AI applications.

What it does

This workflow sets up the core components for an AI Agent:

  1. AI Agent: Initializes a Langchain AI Agent, which acts as the orchestrator for the conversational flow.
  2. OpenAI Chat Model: Defines the language model (specifically, an OpenAI Chat Model) that the AI Agent will use for generating responses.
  3. Simple Memory: Configures a simple memory buffer to allow the AI Agent to retain context from previous turns in a conversation.
  4. Think Tool: Incorporates a 'Think' tool, which can be used by the AI Agent to process thoughts or internal reasoning before generating a response.
  5. AI Agent Tool: Another AI Agent node, likely intended as a sub-agent or a specialized tool within the main agent's capabilities.
  6. If Node: A conditional logic node, which is included but not connected in the provided JSON, suggesting potential for future expansion to route based on specific conditions.
  7. Sticky Note: A simple note for documentation or reminders within the workflow.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n.
  • OpenAI API Key: Required for the "OpenAI Chat Model" node. This key should be configured as an n8n credential.
  • Langchain Nodes: Ensure that the @n8n/n8n-nodes-langchain package is installed and enabled in your n8n instance.

Setup/Usage

  1. Import the Workflow:
    • Download the provided JSON code.
    • In your n8n instance, go to "Workflows" and click "New".
    • Click the "Import from JSON" button and paste the workflow JSON.
  2. Configure Credentials:
    • Locate the "OpenAI Chat Model" node.
    • Ensure your OpenAI API Key is configured as a credential in n8n and selected for this node.
  3. Connect to a Trigger (Optional, for full functionality):
    • This workflow currently defines the core AI agent components but lacks a trigger (e.g., a Webhook, a messaging app trigger) to initiate conversations.
    • To make this agent interactive, you would typically connect a trigger node to the input of the "AI Agent" node (node 1119).
  4. Connect Agent Outputs (Optional, for full functionality):
    • The output of the "AI Agent" node (node 1119) would typically be connected to a node that sends the AI's response back to the user or to another system.
  5. Customize:
    • Adjust the "AI Agent" node's settings (e.g., model, prompt, tools) to define the agent's behavior and capabilities.
    • Modify the "OpenAI Chat Model" node to select different models or parameters.
    • Expand the workflow by connecting the "If" node (node 20) to introduce conditional logic based on the AI's output or user input.

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