Snap & Track Nutrition: Telegram Food Photos → Gemini Vision AI → Google Sheets

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 →

{
  "meta": {
    "templateCredsSetupCompleted": true
  },
  "nodes": [
    {
      "id": "693c6839-0ea4-4261-a0cf-50dd884903c2",
      "name": "Telegram Trigger",
      "type": "n8n-nodes-base.telegramTrigger",
      "position": [
        304,
        0
      ],
      "parameters": {
        "updates": [
          "message"
        ],
        "additionalFields": {
          "download": true
        }
      },
      "credentials": {
        "telegramApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "74d84279-ca5d-4ebe-b9c7-222defe14625",
      "name": "AI Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1040,
        0
      ],
      "parameters": {
        "text": "=You are a nutrition assistant. Your task is to analyze meal description and return structured nutrition data in the exact format below (YAML, JSON, or key-value pairs).\n\nINPUT:\ndescription: {{ $json.content.parts[0].text }}\ndatetime: {{ $now.format('yyyy-MM-dd HH:mm') }}\n\nOUTPUT (copy this format exactly):\n\nmeal_name: \ndate: \nmeal_description: \ncalories: \nprotein: \nfats: \ncarbs: \n\nExample:\nmeal_name: Banana Snack\ndate: 2025-10-24 19:30\nmeal_description: One medium-sized ripe banana with yellow skin and small brown spots (120g)\ncalories: 105\nprotein: 1\nfats: 0\ncarbs: 27\n\nRules:\n- Fill every field (do not leave any blank)\n- Use midpoint for calories/macros if uncertain\n- Keep meal_name short (2-4 words, e.g. \"Chicken Rice Bowl\")\n- Use date in YYYY-MM-DD HH:MM format\n- meal_description should summarize the food, portions, and context in under 15 words\n- Only output the formatted result above\u2014do NOT add extra text or explanations!\n\nIf no food is detected, output:\nmeal_name: Not a meal\ndate: {{ $now.format('yyyy-MM-dd HH:mm') }}\nmeal_description: No human food detected\ncalories: 0\nprotein: 0\nfats: 0\ncarbs: 0\n",
        "options": {},
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 2.2
    },
    {
      "id": "ba964c1d-6535-444b-9f31-b6e401ea7ac4",
      "name": "Google Gemini Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        880,
        208
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "googlePalmApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "7e2cab17-2079-4002-bf35-27e55e78ac21",
      "name": "Analyze an image",
      "type": "@n8n/n8n-nodes-langchain.googleGemini",
      "position": [
        704,
        0
      ],
      "parameters": {
        "text": "You are a Nutrition Vision Assistant. Think like a food scientist and registered dietitian. Reason silently and do not reveal your steps. From a single food photo, identify the meal components, estimate portion weight in grams per component using geometric/visual cues, then compute total calories, protein, carbs, and fat.\n\nEstimation method (internal only; do not output these steps)\n\nIdentify components: list the main foods (e.g., chicken breast, white rice, mixed salad, sauce).\n\nChoose references: map each component to a standard reference food.\n\nEstimate volume/size: use visible objects for scale (plate \u2248 27 cm diameter, fork tines \u2248 3.5 cm, spoon bowl \u2248 5\u20136 cm). Approximate shapes (cuboid, cylinder, dome) to get volume in ml (\u2248 cm\u00b3).\n\nConvert to grams (densities, g/ml): meats 1.05; cooked rice 0.66; cooked pasta 0.60; potato/solid starchy veg 0.80; leafy salad 0.15; sauces creamy 1.00; oils 0.91. If the image clearly suggests deep-fried or glossy/oily coating, account for added oil.\n\nMacros & energy per 100 g (reference values):\n\nWhite rice, cooked: 130 kcal, P 2.7, C 28, F 0.3\n\nPasta, cooked: 131 kcal, P 5.0, C 25, F 1.1\n\nChicken breast, cooked skinless: 165 kcal, P 31, C 0, F 3.6\n\nSalmon, cooked: 208 kcal, P 20, C 0, F 13\n\nLean ground beef (\u224810% fat), cooked: 217 kcal, P 26, C 0, F 12\n\nBlack beans, cooked: 132 kcal, P 8.9, C 23.7, F 0.5\n\nPotato, baked: 93 kcal, P 2.5, C 21, F 0.1\n\nLettuce/leafy salad: 15 kcal, P 1.4, C 2.9, F 0.2\n\nAvocado: 160 kcal, P 2, C 9, F 15\n\nBread (white): 265 kcal, P 9, C 49, F 3.2\n\nEgg, cooked: 155 kcal, P 13, C 1.1, F 11\n\nCheddar cheese: 403 kcal, P 25, C 1.3, F 33\n\nOlive oil: 884 kcal, P 0, C 0, F 100\n(If a food is not listed, pick the closest standard equivalent.)\n\nHidden oil & sauces: if pan-fried or visibly glossy, add ~1 tablespoon oil = 13.5 g = 120 kcal = 13.5 g fat per clearly coated serving; adjust by visual coverage.\n\nSum totals: compute grams per component \u00d7 (per-100 g macros/energy) and add all components.\n\nValidation: enforce Calories \u2248 4\u00d7Protein + 4\u00d7Carbs + 9\u00d7Fat. If off by >8%, adjust fat first (oil/sauce most variable), then carbs (starches), keeping protein consistent with visible lean mass.\n\nRounding: round all final totals to integers. Never output ranges or decimals.\n\nOutput rules (must follow exactly)\n\nPlain text only.\n\nUse this exact structure and field order.\n\nValues are numbers only (no units, no \u201cg\u201d or \u201ckcal\u201d), no extra text, no JSON, no notes.\n\nMeal Description: [short description]\nCalories: [number]\nProteins: [number]\nCarbs: [number]\nFat: [number]",
        "modelId": {
          "__rl": true,
          "mode": "list",
          "value": "models/gemini-2.5-pro",
          "cachedResultName": "models/gemini-2.5-pro"
        },
        "options": {},
        "resource": "image",
        "inputType": "binary",
        "operation": "analyze"
      },
      "credentials": {
        "googlePalmApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "8fd5af40-ff69-4509-b1be-1470773529ff",
      "name": "Send a text message",
      "type": "n8n-nodes-base.telegram",
      "position": [
        1632,
        0
      ],
      "parameters": {
        "text": "={{ $('Analyze an image').item.json.content.parts[0].text }}\n\nImage saved in Google drive, and Data saved in database",
        "chatId": "={{ $('Telegram Trigger').item.json.message.from.id }}",
        "additionalFields": {
          "appendAttribution": false
        }
      },
      "credentials": {
        "telegramApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "f51dfc8d-4414-4524-9da4-159fdfc7b23e",
      "name": "Upload file",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        576,
        -208
      ],
      "parameters": {
        "name": "=Meal_{{ $now.format('yyyy-MM-dd HH:mm') }}",
        "driveId": {
          "__rl": true,
          "mode": "list",
          "value": "My Drive"
        },
        "options": {},
        "folderId": {
          "__rl": true,
          "mode": "list",
          "value": "1p2_SK-Fx8kun4gSxeNeFenFXhtEdpTY0",
          "cachedResultUrl": "https://drive.google.com/drive/folders/1p2_SK-Fx8kun4gSxeNeFenFXhtEdpTY0",
          "cachedResultName": "Cal AI Meals"
        }
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "87716ad6-439c-4926-8cae-d92d343d5fee",
      "name": "Simple Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        1104,
        224
      ],
      "parameters": {
        "sessionKey": "={{ $json.content.parts[0].text }}",
        "sessionIdType": "customKey",
        "contextWindowLength": 60
      },
      "typeVersion": 1.3
    },
    {
      "id": "63d07e12-20ee-4abf-834c-de294c6c646b",
      "name": "Append row in sheet",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        1392,
        0
      ],
      "parameters": {
        "columns": {
          "value": {
            "Date": "={{ $json.output.date }}",
            "Fats": "={{ $json.output.carbs }}",
            "Carbs": "={{ $json.output.fats }}",
            "Calories": "={{ $json.output.calories }}",
            "Proteins": "={{ $json.output.protein }}",
            "Meal_Name": "={{ $json.output.meal_name }}",
            "Meal_description": "={{ $json.output.meal_description }}"
          },
          "schema": [
            {
              "id": "Meal_Name",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Meal_Name",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Date",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Date",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Meal_description",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Meal_description",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Calories",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Calories",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Proteins",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Proteins",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Carbs",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Carbs",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Fats",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Fats",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {},
        "operation": "append",
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 14074018,
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1OmfVqlo8G2ff7895Q-FyqFknJj1k2dYPKM0YpG_vEys/edit#gid=14074018",
          "cachedResultName": "Meals"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1OmfVqlo8G2ff7895Q-FyqFknJj1k2dYPKM0YpG_vEys",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1OmfVqlo8G2ff7895Q-FyqFknJj1k2dYPKM0YpG_vEys/edit?usp=drivesdk",
          "cachedResultName": "Cal AI"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 4.7
    },
    {
      "id": "42132b5e-944f-43aa-9a02-537cb09939b4",
      "name": "Structured Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        1264,
        208
      ],
      "parameters": {
        "jsonSchemaExample": "{\n   \"meal_name\": \"\",\n   \"date\": \"\",\n   \"meal_description\": \"\",\n   \"calories\": \"\",\n   \"protein\": \"\",\n   \"fats\": \"\",\n   \"carbs\": \"\"\n}"
      },
      "typeVersion": 1.3
    },
    {
      "id": "e655016c-c383-4d79-b5d7-c6b17b30258f",
      "name": "Send a text message1",
      "type": "n8n-nodes-base.telegram",
      "position": [
        832,
        -208
      ],
      "parameters": {
        "text": "=The image of the meal is uploaded, please wait till I analyze it and I will give you back the calories and nutriants of the meal. ",
        "chatId": "={{ $('Telegram Trigger').item.json.message.from.id }}",
        "additionalFields": {
          "appendAttribution": false
        }
      },
      "credentials": {
        "telegramApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "01d51b94-531a-4a8a-ae52-9b89daa00bf0",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        848,
        -688
      ],
      "parameters": {
        "color": 4,
        "width": 464,
        "height": 320,
        "content": "# Quick Setup Guide\n## Before You Start - What You Need:\n\n\ud83d\udd17 Telegram Bot (create via @BotFather)\n\n\ud83e\udde0 Google Gemini API key with Vision enabled (get it here)\n\n\ud83d\udd10 Google account for Sheets and Drive access\n\n\ud83d\udcca Basic spreadsheet to track your meals"
      },
      "typeVersion": 1
    },
    {
      "id": "47029503-a844-4d56-a97e-0ca4d8cf2704",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -576,
        -448
      ],
      "parameters": {
        "color": 5,
        "width": 704,
        "height": 1056,
        "content": "# Step-by-Step Setup\n\n## 1. Create Your Telegram Bot\n\n- Open Telegram and message @BotFather\n- Use /newbot command and follow prompts\n- Copy the bot token and add to n8n credentials\n\n## 2. Set Up Google Sheet\n\n- Create a new Google Sheet\n- Add these column headers in row 1:\nMeal_Name | Date | Meal_description | Calories | Proteins | Carbs | Fats\n- Copy the sheet ID from the URL (between /d/ and /edit)\n\n## 3. Create Google Drive Folder\n\n- Create a folder in Google Drive for meal photos (e.g., \"Cal AI Meals\")\n- Copy the folder ID from the URL\n\n## 4. Connect Everything in n8n\n\n- Add Telegram credentials (paste bot token)\n- Add Google Gemini credentials (paste API key)\n- Add Google Sheets OAuth credentials (sign in with Google)\n- Add Google Drive OAuth credentials (use same Google account)\n\n## 5. Update Workflow IDs\n\n- In \"Append row in sheet\" node: select your Google Sheet\n- In \"Upload file\" node: select your Drive folder\n- Save the workflow\n\n## 6. Test It\n\n- Activate the workflow\n- Send your Telegram bot a photo of food\n- Check: photo saved to Drive, data logged to Sheet, reply received"
      },
      "typeVersion": 1
    },
    {
      "id": "24119be9-b4b6-416f-9ff2-973023da2f19",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        688,
        464
      ],
      "parameters": {
        "width": 576,
        "height": 432,
        "content": "## Common First-Time Issues\n\n\u274c No response from bot \u2192 Check bot token is correct and workflow is activated\n\u274c \"Not a meal\" detected \u2192 Ensure photo clearly shows food; avoid blurry/dark images\n\u274c Wrong nutrition values \u2192 AI estimates based on visible portions; works best with clear top-down shots\n\u274c Sheet not updating \u2192 Verify sheet ID and column names match exactly\n\u274c Photo not saving \u2192 Check Google Drive folder ID and OAuth permissions\n\n## What to Test Before Daily Use\n\n\u2705 Send photo of a simple meal (e.g., banana, single ingredient)\n\u2705 Send photo of complex meal (e.g., restaurant plate with multiple items)\n\u2705 Test with different lighting and angles\n\u2705 Verify all data appears correctly in Sheet\n\u2705 Check photos are timestamped properly in Drive\n\u2705 Confirm macro calculations make sense (spot-check a few meals)"
      },
      "typeVersion": 1
    },
    {
      "id": "9ade9c57-c6ec-428d-aaa7-ab34698cca0e",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1904,
        -336
      ],
      "parameters": {
        "color": 6,
        "width": 592,
        "height": 656,
        "content": "# Pro Tips for Better Results\n\n## \ud83d\udcf8 Best photo practices:\n\n- Take photos from directly above (top-down view)\n- Use good lighting\u2014natural light works best\n- Include reference objects (fork, phone) for scale\n- Avoid shadows covering the food\n- Capture the whole plate/bowl in frame\n\n## \ud83c\udfaf Accuracy tips:\n\n- AI is most accurate with whole foods and standard portions\n- Works great for: rice, chicken, vegetables, fruits, standard servings\n- Less accurate for: soups, mixed dishes, tiny snacks\n- For packaged foods: include the label in the photo for better estimates\n\n## \ud83d\udcca Tracking tips:\n\n- Log meals consistently (all meals, not just \"healthy\" ones)\n- Review weekly totals to spot patterns\n- Use Sheet filters/pivots to analyze by date or meal type\n- Set up conditional formatting to highlight high-calorie meals"
      },
      "typeVersion": 1
    }
  ],
  "connections": {
    "AI Agent": {
      "main": [
        [
          {
            "node": "Append row in sheet",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Upload file": {
      "main": [
        [
          {
            "node": "Send a text message1",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Simple Memory": {
      "ai_memory": [
        [
          {
            "node": "AI Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Analyze an image": {
      "main": [
        [
          {
            "node": "AI Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Telegram Trigger": {
      "main": [
        [
          {
            "node": "Analyze an image",
            "type": "main",
            "index": 0
          },
          {
            "node": "Upload file",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Append row in sheet": {
      "main": [
        [
          {
            "node": "Send a text message",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "AI Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Structured Output Parser": {
      "ai_outputParser": [
        [
          {
            "node": "AI Agent",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    }
  }
}

Credentials you'll need

Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.

• googleDriveOAuth2Api
• googlePalmApi
• googleSheetsOAuth2Api
• telegramApi

About this workflow

📸 Snap and send a photo of your meal via Telegram 🧠 Gemini Vision AI analyzes the image and estimates calories, protein, carbs, and fats 🤖 AI Agent structures the data with meal name, description, and timestamp 📄 Auto-logs nutrition data to Google Sheets for tracking 🗂️ Saves…

Source: https://n8n.io/workflows/10277/ — original creator credit. Request a take-down →

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