Building your first WhatsApp chatbot

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Important notice

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

1. Workflow Overview

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help...

Best for

Lead Nurturing automation workflows
AI Chatbot automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.whatsapptrigger, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.toolvectorstore, @n8n/n8n-nodes-langchain.embeddingsopenai, n8n-nodes-base.manualtrigger, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Building your first WhatsApp chatbot
Workflow name: Building your first WhatsApp chatbot

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions.

This template is intended to help introduce n8n users interested in building with WhatsApp.

How it works

This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot.
A product brochure is imported via HTTP request node and its text contents extracted.
The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot.
A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out.
The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool.
The Agent's response is sent back to the user via the WhatsApp node.

How to use

Once you've setup and configured your WhatsApp account and credentials

First, populate the vector store by clicking the "Test Workflow" button.
Next, activate the workflow to enable the WhatsApp chatbot.
Message your designated WhatsApp number and you should receive a message from the AI sales agent.
Tweak datasource and behaviour as required.

Requirements

WhatsApp Business Account
OpenAI for LLM

Customising this workflow

Upgrade the vector store to Qdrant for persistance and production use-cases.
Handle different WhatsApp message types for a more rich and engaging experience for customers.

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 - WhatsApp Trigger

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

Block 2 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1

Block 3 - Window Buffer Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.2

Block 4 - Vector Store Tool

Type / Role: @n8n/n8n-nodes-langchain.toolVectorStore - toolVectorStore
Config choices: Version 1

Block 5 - Embeddings OpenAI

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1

Block 6 - OpenAI Chat Model1

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1

Block 7 - When clicking ‘Test workflow’

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

Block 8 - Embeddings OpenAI1

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1

Block 9 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1

Block 10 - Recursive Character Text Splitter

Type / Role: @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - textSplitterRecursiveCharacterTextSplitter
Config choices: Version 1

Block 11 - Extract from File

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

Block 12 - get Product Brochure

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

Block 13 - Reply To User

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

Block 14 - Reply To User1

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

Block 15 - Product Catalogue

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreInMemory - vectorStoreInMemory
Config choices: Version 1

Block 16 - Sticky Note

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

Block 17 - Sticky Note1

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

Block 18 - Create Product Catalogue

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreInMemory - vectorStoreInMemory
Config choices: Version 1

Block 19 - Sticky Note2

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

Block 20 - Sticky Note3

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

Block 21 - Sticky Note4

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

Block 22 - Sticky Note5

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

Block 23 - Sticky Note6

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

Block 24 - Sticky Note7

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

Showing the first 24 of 28 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Building your first WhatsApp chatbot
Complexity	advanced
Nodes	28
Categories	Lead Nurturing, AI Chatbot
Author	Jimleuk
Published	17 Oct 2024

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2465/2465.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 Lead Nurturing workflows AI Chatbot workflows

Frequently asked questions

What does Building your first WhatsApp chatbot 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 Lead Nurturing, AI Chatbot use case.

Jimleuk

100 workflows

Nodes

n8n-nodes-base.whatsapptrigger @n8n/n8n-nodes-langchain.lmchatopenai @n8n/n8n-nodes-langchain.memorybufferwindow @n8n/n8n-nodes-langchain.toolvectorstore @n8n/n8n-nodes-langchain.embeddingsopenai n8n-nodes-base.manualtrigger @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter

Complexity

advanced

Published 17 Oct 2024

Likes 0

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Install path: /data/workflows/2465/2465.json

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Building your first WhatsApp chatbot

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

How it works

How to use

Requirements

Customising this workflow

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - WhatsApp Trigger

Block 2 - OpenAI Chat Model

Block 3 - Window Buffer Memory

Block 4 - Vector Store Tool

Block 5 - Embeddings OpenAI

Block 6 - OpenAI Chat Model1

Block 7 - When clicking ‘Test workflow’

Block 8 - Embeddings OpenAI1

Block 9 - Default Data Loader

Block 10 - Recursive Character Text Splitter

Block 11 - Extract from File

Block 12 - get Product Brochure

Block 13 - Reply To User

Block 14 - Reply To User1

Block 15 - Product Catalogue

Block 16 - Sticky Note

Block 17 - Sticky Note1

Block 18 - Create Product Catalogue

Block 19 - Sticky Note2

Block 20 - Sticky Note3

Block 21 - Sticky Note4

Block 22 - Sticky Note5

Block 23 - Sticky Note6

Block 24 - Sticky Note7

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