Respond to WhatsApp Messages with AI Like a Pro! — n8n ワークフロー

複雑度 トリガー24個のノード AI👁 91,062回閲覧作成者:Jimleuk

概要

This n8n template demonstrates the beginnings of building your own n8n-powered WhatsApp chatbot! Under the hood, utilise n8n's powerful AI features to handle different message types and use an AI agent to respond to the user. A powerful tool for any use-case!

How it works Incoming WhatsApp Trigger provides a way to get messages into the workflow. The message received is extracted and sent through 1 of 4 branches for processing. Each processing branch uses AI to analyse, summarize or transcribe

使用ノード

HTTP RequestWhatsApp Business CloudAI AgentBasic LLM ChainSimple MemoryWikipediaGoogle Gemini Chat Model

ワークフロープレビュー

1. WhatsApp Trigger
Learn more about the WhatsApp Trigger
To start receiving WhatsApp messages in your work
2. Transcribe Audio Messages 💬
For audio messages or voice notes, we can use GPT4o to
3. Describe Video Messages 🎬
For video messages, one approach is to use a Multimodal
4. Analyse Image Messages 🏞️
For image messages, we can use GPT4o to explain what is
5. Text summarizer 📘
For text messages, we don't need to do much transformat
6. Generate Response with AI Agent
Read more about the AI Agent node
Now that we'll able to handle al
7. Respond to WhatsApp User
Read more about the Whatsapp node
To close out this demonstration, we'll simple send a si
Try It Out!
This n8n template demonstrates the beginnings of buildi
Activate workflow to use!
You must activate the workflow to use this WhatsApp Cha
🚨 Google Gemini Required!
Not using Gemini? Feel free to swap this out for any Mu
🚨 Google Gemini Required!
Not using Gemini? Feel free to swap this out for any Mu
toolmemorymodelmodelmodel
W
WhatsApp Trigger
Get Audio URL
Get Video URL
Get Image URL
Download Video
Download Audio
Download Image
Window Buffer Memory
G
Get User's Message
S
Split Out Message Parts
Wikipedia
R
Redirect Message Types
G
Get Text
Respond to User
Image Explainer
F
Format Response
Google Gemini Chat Model
Google Gemini Audio
Google Gemini Video
Google Gemini Chat Model1
Google Gemini Chat Model2
F
Format Response1
Text Summarizer
AI Agent
24 nodes26 edges

仕組み

  1. 1

    トリガー

    このワークフローは トリガー トリガーで開始します。

  2. 2

    処理

    データは 24 個のノードを流れます, connecting agent, chainllm, httprequest。

  3. 3

    出力

    ワークフローは自動化を完了し、設定された宛先に結果を配信します。

ノード詳細 (24)

HT

HTTP Request

httpRequest

#1
WH

WhatsApp Business Cloud

whatsApp

#2
AI

AI Agent

n8n-nodes-langchain.agent

#3
BA

Basic LLM Chain

n8n-nodes-langchain.chainLlm

#4
SI

Simple Memory

n8n-nodes-langchain.memoryBufferWindow

#5
WI

Wikipedia

n8n-nodes-langchain.toolWikipedia

#6
GO

Google Gemini Chat Model

n8n-nodes-langchain.lmChatGoogleGemini

#7

このワークフローのインポート方法

  1. 1右側の JSONをダウンロード ボタンをクリックしてワークフローファイルを保存します。
  2. 2n8nインスタンスを開き、ワークフロー → 新規 → ファイルからインポート に進みます。
  3. 3ダウンロードした respond-to-whatsapp-messages-with-ai-like-a-pro ファイルを選択し、インポートをクリックします。
  4. 4各サービスノードの 認証情報(APIキー、OAuthなど)を設定します。
  5. 5ワークフローをテスト をクリックして動作確認し、有効化します。

またはn8nの JSONからインポート に直接貼り付け:

{ "name": "Respond to WhatsApp Messages with AI Like a Pro!", "nodes": [...], ...}

インテグレーション

agentchainllmhttprequestlmchatgooglegeminimemorybufferwindowsetsplitoutswitchtoolwikipediawaitwhatsappwhatsapptrigger

このワークフローを取得

ワンクリックでダウンロード&インポート

JSONをダウンロードn8n.ioで見る
ノード24
複雑度high
トリガーtrigger
閲覧数91,062
カテゴリAI

作成者

Jimleuk

Jimleuk

@jimleuk

タグ

agentchainllmhttprequestlmchatgooglegeminimemorybufferwindowsetsplitoutswitchtoolwikipediawait

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