This article provides a complete, practical walkthrough of the Telegram Splitout Create Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   📊 Automating Nutrition Tracking from Voice or Text with n8n and OpenAI
   
   Meta Description:
   Discover how to automate meal nutrition tracking using Telegram, OpenAI GPT-4o, and Google Sheets with n8n. This custom workflow lets users log meals via voice or text and get detailed nutrient breakdowns instantly.
   
   Keywords:
   n8n workflow, OpenAI GPT-4o, Telegram bot, nutrition tracker, automate food logging, Google Sheets automation, voice to text, AI meal analysis, dietary intake tracking, LangChain, AI for nutrition
   
   Third-Party APIs Used:
   
   1. Telegram Bot API – for receiving and sending messages including voice inputs.
   2. OpenAI API – used for GPT-4o-mini model to generate nutritional analysis and transcribe audio messages.
   3. Google Sheets API – for storing structured nutritional data for long-term tracking.
   
   —
   
   Article:
   
   🎙️ Automating Nutrition Tracking with AI: From Meal Descriptions to Data Analytics Using n8n
   
   Maintaining a proper nutritional log can be a game-changer for anyone striving for a healthier lifestyle. However, manually writing down every meal and calculating calories, carbs, and other nutrients can feel like too much effort — until now. Thanks to the power of n8n, OpenAI, and some smart workflow design, you can now send a quick voice or text message and have your entire meal analyzed automatically and stored in Google Sheets.
   
   Let’s dive into how this works.
   
   🎯 The Goal
   
   This n8n workflow was designed to automate the process of logging meals and their nutrition content. It enables users to:
   
   - Send a text or voice message via Telegram describing their meal.
   - Use OpenAI to analyze the message and estimate key nutrient content.
   - Save the nutritional breakdown into a Google Sheet for later review or analysis.
   
   💡 How It Works — Step by Step
   
   1. Telegram Bot as Entry Point
   
   The entire process begins with a Telegram bot created using Telegram's Bot API. Users interact with the bot by sending a message — either typed or recorded. The bot listens for incoming messages using the Telegram Trigger node.
   
   2. Detecting Voice Messages
   
   The bot differentiates between text and voice using an If node. If it’s a voice message, it goes through OpenAI’s Whisper model via the “Transcribe Recording” node to convert it into text.
   
   3. Standardizing the Input
   
   Whether from text or transcribed voice, a Set node transforms the message into a standardized variable called chatInput that will be passed into the AI model.
   
   4. AI-Powered Nutritional Analysis
   
   This is where the magic happens. The AI Agent node (powered by OpenAI via LangChain) receives the user message and is prompted with a tailored instruction:
   
   > “Approximate the kcals, protein, carbohydrates, lipids (fats), and electrolyte (sodium, potassium, magnesium, zinc, and iron) content in the following dietary intake…”
   
   The model uses its built-in knowledge to infer typical values based on standard foods. It even provides its reasoning for the calculations.
   
   5. Parsing the Output
   
   To ensure structured and consistent outputs, the Langchain Output Parser node validates and parses the AI-generated response into JSON format. Each nutrient item includes the name, quantity, and unit.
   
   6. Preparing Data for Storage
   
   Next, the JSON results are split into individual items via the Explode node. A Code node appends the current date in a spreadsheet-compatible format based on the Excel serial date system.
   
   7. Data Logging in Google Sheets
   
   Individual nutrient data points are passed into the Google Sheets node, where they are logged into a designated sheet for tracking over time. Each entry includes:
   
   - Name of the nutrient
   - Quantity
   - Measurement unit (e.g., grams, kcal)
   - Date of entry
   
   8. Friendly Feedback
   
   After successful entry, the workflow sends a Telegram message back to the user saying, “Your meal has been saved.” This instant feedback ensures users feel that their data has been successfully logged.
   
   🔗 APIs and Services That Power the Workflow
   
   This powerful automation stack relies on several third-party services:
   
   - Telegram Bot API – Handles all user interaction, including sending and receiving messages.
   - OpenAI API – Performs two tasks: transcribes voice messages (Whisper) and analyzes nutritional content using GPT-4o-mini via LangChain.
   - Google Sheets API – Serves as the database, logging and storing every analyzed data point.
   
   🛠️ Customizations and Extensions
   
   The base workflow is very extensible. Here are a few ideas:
   
   - Upgrade to the Pro version that uses the official USDA nutrition database for more accurate data.
   - Send the AI's full reasoning back to the user.
   - Visualize the user’s nutrition over time using Data Studio or another BI tool connected to Google Sheets.
   - Add daily or weekly reminders to log meals via Telegram.
   
   📌 Final Thoughts
   
   This workflow is a simple yet powerful demonstration of how AI and automation can solve everyday problems like nutrition tracking. Whether you're a developer curious about n8n, a fitness enthusiast, or someone who wants to take food journaling to the next level, this solution offers a hands-free, intelligent way to eat more mindfully.
   
   To get started, follow the setup guide included in the workflow (spoiler: it’s just setting up Telegram, OpenAI, and connecting your Google Sheet).
   
   You can even contact the creator at thomas@pollup.net if you need help or want a pro version with advanced features. Automation like this makes the future of health tracking look very bright indeed. 🌟
   
   —
   
   Written with automation and AI at its core 🧠🤖

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.