Answer Telegram customer FAQs with Gemini and Google Sheets
n8n-workflow-20628.n8n.io
· n8n.io
Quick overview This workflow listens for incoming Telegram messages, pulls an FAQ from Google Sheets, and uses Google Gemini to reply in the customer’s language while keeping short chat history. It classifies whether a human needs to step in, logs each exchange to Sheets, and alerts the owner on Telegram when needed. How it works 1. Triggers when a customer sends a message to your Telegram bot. 2. Loads your business details and reads the full FAQ table from Google Sheets. 3. Uses Google Gemini (with a 10-message chat memory per chat) to generate a reply strictly from the FAQ and business info, then sends the response back to the customer on Telegram. 4. Uses Google Gemini again to classify the exchange as “Answered” or “Needs a person” based on the customer message and the bot’s reply. 5. Appends the conversation details to a Google Sheets Log tab, marking whether human help is required. 6. If the message needs a person, sends a Telegram alert to the owner and logs the handoff to Goog
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Conformance score: 60/100
Catalogue listing, not an A2A agent: a workflow template listed by n8n.io. Scored on the 2 criteria that apply (9 of 15 points, scaled to 100); the A2A card criteria do not apply.
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last 24h · 0 invocations Public aggregate · no PII recordedNothing observed in the last 7 days — no invocations, no lookups, no listing impressions. Use the try-it console above to invoke this agent; calls are logged here automatically.
Listing history
1 snapshot Every change to the catalogue entry| Captured | Hash | Snapshot |
|---|---|---|
| 2026-10-19 15:55:20 current | b93d1543a216… |
view → |
Endpoints
| Agent card | https://n8n.io/workflows/20628 |
| Provider | https://n8n.io |
| Docs | https://n8n.io/workflows/20628 |
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"typeVersion": 1,
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"id": 6,
"name": "Communication"
},
{
"id": 28,
"name": "HITL"
}
]
},
{
"id": 50,
"icon": "file:telegram.svg",
"name": "n8n-nodes-base.telegramTrigger",
"codex": {
"data": {
"resources": {
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{
"url": "https://n8n.io/blog/create-a-toxic-language-detector-for-telegram/",
"icon": "\ud83e\udd2c",
"label": "Create a toxic language detector for Telegram in 4 step"
},
{
"url": "https://n8n.io/blog/automatically-adding-expense-receipts-to-google-sheets-with-telegram-mindee-twilio-and-n8n/",
"icon": "\ud83e\uddfe",
"label": "Automatically Adding Expense Receipts to Google Sheets with Telegram, Mindee, Twilio, and n8n"
},
{
"url": "https://n8n.io/blog/how-to-set-up-a-ci-cd-pipeline-with-no-code/",
"icon": "\ud83c\udfa1",
"label": "How to set up a no-code CI/CD pipeline with GitHub and TravisCI"
},
{
"url": "https://n8n.io/blog/creating-telegram-bots-with-n8n-a-no-code-platform/",
"icon": "\ud83d\udcac",
"label": "Creating Telegram Bots with n8n, a No-Code Platform"
},
{
"url": "https://n8n.io/blog/aws-workflow-automation/",
"label": "7 no-code workflow automations for Amazon Web Services"
}
],
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/trigger-nodes/n8n-nodes-base.telegramtrigger/"
}
],
"credentialDocumentation": [
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"url": "https://docs.n8n.io/integrations/builtin/credentials/telegram/"
}
]
},
"categories": [
"Communication"
],
"nodeVersion": "1.0",
"codexVersion": "1.0"
}
},
"group": "[\"trigger\"]",
"defaults": {
"name": "Telegram Trigger"
},
"iconData": {
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},
"displayName": "Telegram Trigger",
"typeVersion": 1,
"nodeCategories": [
{
"id": 6,
"name": "Communication"
}
]
},
{
"id": 565,
"icon": "fa:sticky-note",
"name": "n8n-nodes-base.stickyNote",
"codex": {
"data": {
"alias": [
"Comments",
"Notes",
"Sticky"
],
"categories": [
"Core Nodes"
],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Helpers"
]
}
}
},
"group": "[\"input\"]",
"defaults": {
"name": "Sticky Note",
"color": "#FFD233"
},
"iconData": {
"icon": "sticky-note",
"type": "icon"
},
"displayName": "Sticky Note",
"typeVersion": 1,
"nodeCategories": [
{
"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 1119,
"icon": "fa:robot",
"name": "@n8n/n8n-nodes-langchain.agent",
"codex": {
"data": {
"alias": [
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"Chat",
"Conversational",
"Plan and Execute",
"ReAct",
"Tools"
],
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/"
}
]
},
"categories": [
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"Langchain"
],
"subcategories": {
"AI": [
"Agents",
"Root Nodes"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "AI Agent",
"color": "#404040"
},
"iconData": {
"icon": "robot",
"type": "icon"
},
"displayName": "AI Agent",
"typeVersion": 3,
"nodeCategories": [
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"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1163,
"icon": "fa:database",
"name": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.memorybufferwindow/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Memory"
],
"Memory": [
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]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Simple Memory"
},
"iconData": {
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"type": "icon"
},
"displayName": "Simple Memory",
"typeVersion": 1,
"nodeCategories": [
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"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
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"icon": "file:aggregate.svg",
"name": "n8n-nodes-base.aggregate",
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"data": {
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"Flatten",
"Transform",
"Array",
"List",
"Item"
],
"details": "",
"resources": {
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"primaryDocumentation": [
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"url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.aggregate/"
}
]
},
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],
"nodeVersion": "1.0",
"codexVersion": "1.0",
"subcategories": {
"Core Nodes": [
"Data Transformation"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Aggregate"
},
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},
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"typeVersion": 1,
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"id": 9,
"name": "Core Nodes"
}
]
},
{
"id": 1262,
"icon": "file:google.svg",
"name": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatgooglegemini/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Language Models",
"Root Nodes"
],
"Language Models": [
"Chat Models (Recommended)"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Google Gemini Chat Model"
},
"iconData": {
"type": "file",
"fileBuffer": "data:image/svg+xml;base64,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"
},
"displayName": "Google Gemini Chat Model",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
},
{
"id": 1265,
"icon": "fa:tags",
"name": "@n8n/n8n-nodes-langchain.textClassifier",
"codex": {
"data": {
"resources": {
"primaryDocumentation": [
{
"url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.text-classifier/"
}
]
},
"categories": [
"AI",
"Langchain"
],
"subcategories": {
"AI": [
"Chains",
"Root Nodes"
]
}
}
},
"group": "[\"transform\"]",
"defaults": {
"name": "Text Classifier"
},
"iconData": {
"icon": "tags",
"type": "icon"
},
"displayName": "Text Classifier",
"typeVersion": 1,
"nodeCategories": [
{
"id": 25,
"name": "AI"
},
{
"id": 26,
"name": "Langchain"
}
]
}
]
},
"detail": {
"description": "Quick overview This workflow listens for incoming Telegram messages, pulls an FAQ from Google Sheets, and uses Google Gemini to reply in the customer\u2019s language while keeping short chat history. It classifies whether a human needs to step in, logs each exchange to Sheets, and alerts the owner on Telegram when needed. How it works 1. Triggers when a customer sends a message to your Telegram bot. 2. Loads your business details and reads the full FAQ table from Google Sheets. 3. Uses Google Gemini (with a 10-message chat memory per chat) to generate a reply strictly from the FAQ and business info, then sends the response back to the customer on Telegram. 4. Uses Google Gemini again to classify the exchange as \u201cAnswered\u201d or \u201cNeeds a person\u201d based on the customer message and the bot\u2019s reply. 5. Appends the conversation details to a Google Sheets Log tab, marking whether human help is required. 6. If the message needs a person, sends a Telegram alert to the owner and logs the handoff to Google Sheets. Setup 1. Create a Telegram bot with @BotFather and add Telegram credentials for the Telegram Trigger and Telegram send-message nodes. 2. Add a Google Gemini API credential (from Google AI Studio) and connect it to both Gemini nodes. 3. Add Google Sheets credentials and create a spreadsheet with a \u201cFAQ\u201d tab (Question, Answer columns) and a \u201cLog\u201d tab (Time, Customer, Message, Reply, Needs a person columns). 4. In the workflow settings fields, fill in your owner Telegram chat ID plus the FAQ and Log Google Sheets document IDs. Requirements - A Telegram bot created with @BotFather - A Google Gemini API key (the free tier is enough to start) - A Google account for Sheets, with a FAQ tab and a Log tab Customization - Change the categories in Needs a person? to decide which messages reach you. - Add or edit rows in the FAQ tab at any time; the next message already uses them, with no workflow edits."
}
}