Automating WhatsApp replies using Go High Level with Redis and Anthropic

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Important notice

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

1. Workflow 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,...

Best for

AI Chatbot automation workflows
Multimodal AI automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.webhook, n8n-nodes-base.switch, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatanthropic, n8n-nodes-base.httprequest, n8n-nodes-base.wait, n8n-nodes-base.if, n8n-nodes-base.noop

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Jorge Martínez.

Original n8n.io source

1.1 Workflow description

Title: Automating WhatsApp replies using Go High Level with Redis and Anthropic
Workflow name: Automating WhatsApp replies using Go High Level with Redis and Anthropic

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

Receive message in n8n via Webhook from GHL (Customer Replied (SMS) automation). WhatsApp messages arrive the same way using the Wazzap plugin.
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.
Buffer messages in Redis to group multiple messages sent in a short window.
Run AI Agent using the ClientInfo tool to answer only with accurate service/branch data.
Sanitize AI output before sending back.
Update GHL contact custom field (IA_answer) with the AI’s response.
Send SMS reply automatically via GHL’s outbound automation triggered by the updated custom field.

How to set up

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.
Connect Wazzap in GHL to handle WhatsApp messages.
Configure Redis in n8n (host, port, DB index, password).
Add your AI model credentials (Anthropic, OpenAI, etc.) in n8n.
(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.

1.2 Logical Blocks

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.

2. Block-by-Block Analysis

Block 1 - Webhook

Type / Role: n8n-nodes-base.webhook - webhook
Config choices: Version 2

Block 2 - Switch1

Type / Role: n8n-nodes-base.switch - switch
Config choices: Version 3

Block 3 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.7

Block 4 - Anthropic Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatAnthropic - lmChatAnthropic
Config choices: Version 1.3

Block 5 - Update Contact

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 6 - GHL Custom fields

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 7 - Wait

Type / Role: n8n-nodes-base.wait - wait
Config choices: Version 1.1

Block 8 - If

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 9 - No Operation, do nothing

Type / Role: n8n-nodes-base.noOp - noOp
Config choices: Version 1

Block 10 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 11 - Save message

Type / Role: n8n-nodes-base.redis - redis
Config choices: Version 1

Block 12 - Get messages

Type / Role: n8n-nodes-base.redis - redis
Config choices: Version 1

Block 13 - Sanitize and Format Message Body

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 14 - Delete messages

Type / Role: n8n-nodes-base.redis - redis
Config choices: Version 1

Block 15 - messages

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 16 - When Executed by Another Workflow

Type / Role: n8n-nodes-base.executeWorkflowTrigger - executeWorkflowTrigger
Config choices: Version 1.1

Block 17 - Download file

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 18 - Success

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 19 - Merge

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.2

Block 20 - Call n8n Workflow Tool

Type / Role: @n8n/n8n-nodes-langchain.toolWorkflow - toolWorkflow
Config choices: Version 2.2

Block 21 - Extract Tests

Type / Role: n8n-nodes-base.extractFromFile - extractFromFile
Config choices: Version 1

Block 22 - Extract Sites

Type / Role: n8n-nodes-base.extractFromFile - extractFromFile
Config choices: Version 1

Block 23 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 24 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Showing the first 24 of 31 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Automating WhatsApp replies using Go High Level with Redis and Anthropic
Complexity	advanced
Nodes	31
Categories	AI Chatbot, Multimodal AI
Author	Jorge Martínez
Published	09 Aug 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/7191/7191.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

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.

Download workflow JSON Original n8n.io source AI Chatbot workflows Multimodal AI workflows

Frequently asked questions

What does Automating WhatsApp replies using Go High Level with Redis and Anthropic do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

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.

Jorge Martínez

5 workflows

Nodes

n8n-nodes-base.webhook n8n-nodes-base.switch @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatanthropic n8n-nodes-base.httprequest n8n-nodes-base.wait n8n-nodes-base.if n8n-nodes-base.noop

Complexity

advanced

Published 09 Aug 2025

Likes 0

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Install path: /data/workflows/7191/7191.json

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Automating WhatsApp replies using Go High Level with Redis and Anthropic

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Automating WhatsApp replies in Go High Level with Redis and Anthropic

Description

Who’s it for

How it works / What it does

How to set up

Requirements

How to customize the workflow

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Webhook

Block 2 - Switch1

Block 3 - AI Agent

Block 4 - Anthropic Chat Model

Block 5 - Update Contact

Block 6 - GHL Custom fields

Block 7 - Wait

Block 8 - If

Block 9 - No Operation, do nothing

Block 10 - Simple Memory

Block 11 - Save message

Block 12 - Get messages

Block 13 - Sanitize and Format Message Body

Block 14 - Delete messages

Block 15 - messages

Block 16 - When Executed by Another Workflow

Block 17 - Download file

Block 18 - Success

Block 19 - Merge

Block 20 - Call n8n Workflow Tool

Block 21 - Extract Tests

Block 22 - Extract Sites

Block 23 - Sticky Note

Block 24 - Sticky Note1

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions