Automate Multilingual Slack Communication (JA ⇄ EN) with Gemini 2.5 Flash — Flujo de trabajo n8n

Alta complejidad🔗 Webhook24 nodos🏷️ Miscellaneous👁 2 vistaspor Tomohiro Goto

Descripción general

🧠 How it works
This workflow automatically translates messages between Japanese and English inside Slack — perfect for mixed-language teams.
In our real-world use case, our 8-person team includes Arif, an English-speaking teammate from Indonesia, while the rest mainly speak Japanese.

Before using this workflow, our daily chat often included:
“Can someone translate this for Arif?”
“I don’t understand what Arif wrote — can someone summarize it in Japanese?”
“I need to post this announ

Nodos utilizados

HTTP RequestCodeBasic LLM ChainGoogle Gemini Chat Model

Vista previa del flujo de trabajo

Slack Multilingual Assistant (Gemini 2.5 Flas
🔧 Three translation modes unified in one workflow:
1️⃣ /trans — Public bilingual announcements
  Format: 【from @user】 Original ---------- Translation
🔧 Create and Configure your Slack App (Trans
1️⃣ Create App
 → From scratch → Name it “TransBot” → Select your work
2️⃣ Add Bot Token Scopes in OAuth & Permissions
 • commands
 • chat
🔁 Workflow Architecture
• Webhooks:
  – /slack/trans → Slash Command
  – /slack/mention → app_mention & reaction_added even
• Language Model: Google Gemini 2.5 Flash
• Logic:
🚨 Common Errors & Fixes
❌ invalid_auth
 → Using an old token or bot not invited to channel
  Fix: Reinstall app / use latest xoxb token / `/invite
❌ missing_scope
 → A required scop
✅ Workflow is Active in n8n
✅ Slack Event Subscription Request URL shows “Verified
✅ Bot Token includes chat:write, channels:history, reac
✅ /trans Slash Command responds with “Tran
modelmodelmodel
⚡
W
Webhook (Slack /trans)
⚡
A
Ack (Respond to Slack)
Parse Slash Payload
⚡
A
Ack Mention Event
Parse Mention Event
⚡
A
Ack Reaction Event
Parse Reaction Event
Fetch Original Message
Prep Reaction Translatio…
Google Gemini Chat Model
Basic LLM Chain (/trans)
Basic LLM Chain (@trans)
Basic LLM Chain (reaction)
Format Slack Message (/t…
Format Slack Message (@t…
Format Slack Message (re…
Post to Slack (response_…
Post to Slack (@trans re…
Post to Slack (reaction …
⚡
W
Webhook (Slack @trans + …
R
Route by Event Type
S
Skip Reaction When No Me…
Filter Reaction Type
Detect Slack Event Type
24 nodes24 edges

Cómo funciona

  1. 1

    Disparador

    El flujo de trabajo comienza con un disparador webhook.

  2. 2

    Procesamiento

    Los datos fluyen a través de 24 nodos, connecting chainllm, code, httprequest.

  3. 3

    Salida

    El flujo de trabajo completa su automatización y entrega el resultado al destino configurado.

Detalles de nodos (24)

HT

HTTP Request

httpRequest

#1
CO

Code

code

#2
BA

Basic LLM Chain

n8n-nodes-langchain.chainLlm

#3
GO

Google Gemini Chat Model

n8n-nodes-langchain.lmChatGoogleGemini

#4

Cómo importar este flujo de trabajo

  1. 1Haz clic en el botón Descargar JSON a la derecha para guardar el archivo del flujo de trabajo.
  2. 2Abre tu instancia de n8n. Ve a Flujos de trabajo → Nuevo → Importar desde archivo.
  3. 3Selecciona el archivo automate-multilingual-slack-communication-ja-en-with-gemini-25-flash descargado y haz clic en Importar.
  4. 4Configura las credenciales para cada nodo de servicio (claves API, OAuth, etc.).
  5. 5Haz clic en Probar flujo de trabajo para verificar que todo funcione, luego actívalo.

O pega directamente en n8n → Importar desde JSON:

{ "name": "Automate Multilingual Slack Communication (JA ⇄ EN) with Gemini 2.5 Flash", "nodes": [...], ...}

Integraciones

chainllmcodehttprequestiflmchatgooglegeminirespondtowebhookswitchwebhook

Obtener este flujo de trabajo

Descarga e importa con un solo clic

Descargar JSONVer en n8n.io
Nodos24
Complejidadhigh
Disparadorwebhook
Vistas2
CategoríaMiscellaneous

Creado por

Tomohiro Goto

Tomohiro Goto

@taoo

Etiquetas

chainllmcodehttprequestiflmchatgooglegeminirespondtowebhookswitchwebhook
⚡

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