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Automating WhatsApp replies using Go High Level with Redis and Anthropic

Workflow preview

Automating WhatsApp replies using Go High Level with Redis and Anthropic preview
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Important notice

This workflow is provided as-is. Please review and test before using in production.

Overview

Automating WhatsApp replies in Go High Level with Redis and Anthropic

Description

  • Integrates GHL + Wazzap with Redis and an AI Agent using ClientInfo to process messages, generate accurate replies, and send them via a custom field trigger.

Who’s it for

  • This workflow is for businesses using GoHighLevel (GHL), including the Wazzap plugin for WhatsApp, who want to automate inbound SMS/WhatsApp replies with AI. It’s ideal for teams that need accurate, data-driven responses from a predefined ClientInfo source and want to send them back to customers without paying for extra inbound automations.

How it works / What it does

  1. Receive message in n8n via Webhook from GHL (Customer Replied (SMS) automation). WhatsApp messages arrive the same way using the Wazzap plugin.
  2. Filter message type:
    • If audio → skip processing and send fallback asking for text.
    • If text → sanitize by fixing escaped quotes, escaping line breaks/carriage returns/tabs, and removing invalid fields.
  3. Buffer messages in Redis to group multiple messages sent in a short window.
  4. Run AI Agent using the ClientInfo tool to answer only with accurate service/branch data.
  5. Sanitize AI output before sending back.
  6. Update GHL contact custom field (IA_answer) with the AI’s response.
  7. Send SMS reply automatically via GHL’s outbound automation triggered by the updated custom field.

How to set up

  1. In GHL, create:
    • Inbound automation: Trigger on Customer Replied (SMS) → Send to your n8n Webhook.
    • Outbound automation: Trigger when IA_answer is updated → Send SMS to the contact.
    • Create a custom field named IA_answer.
  2. Connect Wazzap in GHL to handle WhatsApp messages.
  3. Configure Redis in n8n (host, port, DB index, password).
  4. Add your AI model credentials (Anthropic, OpenAI, etc.) in n8n.
  5. (Optional) Set up the Google Drive Excel Merge sub-workflow to enrich ClientInfo with external data.

Requirements

  • GoHighLevel sub-account API key.
  • Anthropic (Claude) API key or another supported LLM provider.
  • Redis database for temporary message storage.
  • GHL automations: one for inbound messages to n8n, one for outbound replies when IA_answer is updated.
  • GHL custom field: IA_answer to store and trigger replies.
  • Wazzap plugin in GHL for WhatsApp message handling.

How to customize the workflow

  • Add more context or business-specific data to the AI Agent prompt so replies match your brand tone and policies.
  • Expand the ClientInfo dataset with additional services, branches, or product details.
  • Adjust the Redis wait time to control how long the workflow buffers messages before replying.