Gerald Denor This workflow corresponds to n8n.io template #9294 — we link there as the canonical source.
This workflow follows the Agent → Datatable recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
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},
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},
"getUserData": {
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{
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},
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{
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{
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},
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{
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{
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},
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{
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},
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{
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{
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},
"Simple Memory2": {
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{
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]
},
"Simple Memory3": {
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{
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},
"Simple Memory4": {
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{
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},
"Clear the State": {
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{
"node": "Create Success Message",
"type": "main",
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]
},
"Parse AI Output": {
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[
{
"node": "Save Updated Meal",
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}
]
]
},
"Re-Analyze Meal": {
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{
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}
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},
"Append Meal Data": {
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{
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}
]
]
},
"Telegram Trigger": {
"main": [
[
{
"node": "Typing\u2026",
"type": "main",
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},
{
"node": "Standardize Input",
"type": "main",
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}
]
]
},
"fetchmealdetails": {
"ai_tool": [
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{
"node": "Update Meal Agent (Step 3 - Final Output)",
"type": "ai_tool",
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}
]
]
},
"Save Updated Meal": {
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[
{
"node": "Clear the State",
"type": "main",
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}
]
]
},
"Standardize Input": {
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{
"node": "Is User Registered?",
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}
]
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},
"Sequential Routing": {
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{
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{
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],
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{
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],
[
{
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"The Log Meal Agent": {
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},
"getDailyMealReport": {
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"Cal AI Router Agent": {
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},
"Is User Registered?": {
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{
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},
"Update Profile Data": {
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[
{
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"type": "ai_tool",
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},
"Analyze Text Message": {
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{
"node": "Format Text Analysis",
"type": "main",
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},
"Format Text Analysis": {
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{
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},
"Input Message Router": {
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[
{
"node": "Analyze Text Message",
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],
[
{
"node": "Download Voice Message",
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"index": 0
}
],
[
{
"node": "Download IMAGE",
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}
],
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{
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{
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},
"State-Setting Node #1": {
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{
"node": "Send a text message2",
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}
]
]
},
"logMealAnalysis(tool)": {
"ai_tool": [
[
{
"node": "The Log Meal Agent",
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]
]
},
"Create Success Message": {
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{
"node": "MarkdownV2",
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}
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]
},
"Download Voice Message": {
"main": [
[
{
"node": "Fix mime",
"type": "main",
"index": 0
}
]
]
},
"Get Conversation State": {
"main": [
[
{
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"type": "main",
"index": 0
}
]
]
},
"get_message (register)": {
"main": [
[
{
"node": "Register Agent",
"type": "main",
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}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Update Meal Agent (Step 3 - Final Output)",
"type": "ai_languageModel",
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},
{
"node": "Update Meal Agent (Step 1 - Ask for ID)",
"type": "ai_languageModel",
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},
{
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}
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]
},
"Set Update Profile State": {
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{
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}
]
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},
"Conversation State Router": {
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{
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],
[
{
"node": "Update Meal Agent (Step 3 - Final Output)",
"type": "main",
"index": 0
}
],
[
{
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],
[
{
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]
},
"Google Gemini Chat Model1": {
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},
"Google Gemini Chat Model2": {
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{
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"type": "ai_languageModel",
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},
"Google Gemini Chat Model3": {
"ai_languageModel": [
[
{
"node": "The Log Meal Agent",
"type": "ai_languageModel",
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}
]
]
},
"Google Gemini Chat Model4": {
"ai_languageModel": [
[
{
"node": "Report Agent",
"type": "ai_languageModel",
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},
{
"node": "Profile Agent",
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}
]
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},
"get_message (Media message)": {
"main": [
[
{
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"type": "main",
"index": 2
}
]
]
},
"get_message (Audio/Video message)": {
"main": [
[
{
"node": "Merge2",
"type": "main",
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}
]
]
},
"New Description State-Setting Node #2": {
"main": [
[
{
"node": "Send a text message3",
"type": "main",
"index": 0
}
]
]
},
"Update Meal Agent (Step 1 - Ask for ID)": {
"main": [
[
{
"node": "MarkdownV3",
"type": "main",
"index": 0
}
]
]
},
"Update Meal Agent (Step 2 - Ask for Desc)": {
"main": [
[
{
"node": "MarkdownV4",
"type": "main",
"index": 0
}
]
]
},
"Update Meal Agent (Step 3 - Final Output)": {
"main": [
[
{
"node": "Re-Analyze Meal",
"type": "main",
"index": 0
}
]
]
}
}
}
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How this works
Users who want to track calories and log meals without opening an app can send photos or text descriptions directly through Telegram, where the workflow analyses the content with Gemini AI, calculates nutritional values, and stores the results in a data table for later review. The key step is the AI agent that interprets the input, decides whether to ask clarifying questions or record the entry, and maintains conversation context across messages. This approach suits anyone who prefers quick voice or photo logging over manual entry in spreadsheets or dedicated fitness apps.
Use it for daily personal tracking or small-scale coaching setups where flexibility matters more than strict data validation. Avoid it for clinical nutrition programmes or large teams needing audit trails and export controls. A common variation replaces the memory buffer with a database lookup to pull previous entries before the AI decides on the next action.
About this workflow
A comprehensive n8n workflow demonstrating advanced AI agent orchestration, stateful conversation management, and multi-modal input processing for nutrition tracking applications.
Source: https://n8n.io/workflows/9294/ — original creator credit. Request a take-down →
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