This article provides a complete, practical walkthrough of the Telegram Splitout Monitor Scheduled 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:  
   Automated Expense Tracking: Telegram Invoices to Notion with AI-Powered Summaries and Visual Reports Using n8n
   
   Meta Description:  
   Discover how to automate financial tracking by turning Telegram invoice photos into structured Notion records with Google Gemini AI summaries and automated periodic spending reports—complete with charts.
   
   Keywords:  
   n8n financial automation, Telegram receipts to Notion, Google Gemini AI, invoice data extraction, expense tracking automation, Notion budget tracker, AI invoice parser, Telegram bot finance, report generation n8n, quickchart integration
   
   Third-Party APIs Used:
   
   1. Telegram API – for receiving invoice photos from users and sending back summaries/charts.
   2. Google Gemini (formerly PaLM) API – for extracting and summarizing invoice details using AI.
   3. Notion API – for storing structured expense data into a Notion database.
   4. QuickChart API – for generating visual spending reports (bar charts) based on recent Notion data.
   
   Article Body:
   
   In today’s digital landscape, efficient personal finance management often involves tedious manual entry—collecting invoices, categorizing expenses, and updating budget databases. But what if you could simply send a photo of your receipt to a Telegram bot and get structured, categorized expense entries saved directly to Notion, complete with AI-generated summaries and weekly visual reports?
   
   Enter the “Telegram to Notion Financial Tracker” – a powerful automated workflow built on n8n, leveraging AI and modern APIs to effortlessly manage your finances.
   
   📥 From Invoice Photos to Insightful Data  
   
   The automation starts when a user sends a photo of an invoice or receipt via Telegram. This image is intercepted by a Telegram Trigger node in n8n. The bot then downloads the highest-resolution version of the uploaded image and prepares it for processing.
   
   🧠 AI Data Extraction with Google Gemini  
   
   Next, the image is routed through a Google Gemini-powered LLM (Large Language Model) chain integrated through the LangChain plugin in n8n. Customized prompting guides the AI to detect and summarize fields such as:
   
   - Item name  
   - Quantity  
   - Price per unit  
   - Total cost  
   - Category (e.g., Food & Beverage, Healthcare)  
   - Any included tax  
   - Total invoice cost  
   - Date (auto-filled if not found)
   
   The AI returns the output in a structured JSON format, along with a short natural-language summary (e.g., “You spent a total of $24.50 on 3 items, including 10% tax.”).
   
   🗃️ Saving Structured Data to Notion  
   
   The parsed data is then validated against a predefined schema to ensure consistency. Each expense item gets its own row in a Notion database, with appropriate fields like Date, Category, Total, Tax, and Message Summary. This Notion database becomes the single source of truth for your expenses.
   
   💬 Instant Confirmation on Telegram  
   
   After recording the expenses, the bot sends a concise message back to the user via Telegram, summarizing the details extracted from the image: items purchased, total amount spent, and any tax applied.
   
   📊 Weekly Reports with Visual Charts  
   
   To maintain visibility, the system includes a scheduled report generator. Once a week (or any frequency you configure), n8n triggers a sequence that:
   
   1. Fetches the past week’s transactions from the Notion database.
   2. Sums up expenses by category (e.g., Food & Beverage vs Transportation).
   3. Generates a bar chart using QuickChart, visualizing the spending distribution.
   4. Sends the chart back to a designated Telegram chat or channel.
   
   All of this happens automatically—no manual effort required.
   
   🛠️ Why This Setup Is Powerful:
   
   - Uses AI to interpret unstructured data (images) accurately
   - Offers both transparency and traceability via Notion
   - Sends visual reports for easy budgeting insight
   - Easily extendable/customizable in n8n
   - Saves hours of manual expense logging
   
   🔧 Customize It Your Way  
   
   The beauty of this workflow lies in its flexibility. You can:
   
   - Change the reporting frequency (daily, monthly)
   - Train the AI to understand new invoice formats
   - Modify the Notion schema for more complex accounting
   - Route reports to different chats for family or team members
   
   🌐 API Integrations at Play  
   
   To orchestrate this frictionless pipeline, four major APIs are interconnected via n8n:
   
   1. Telegram API – Listens for photo messages and sends responses.
   2. Google Gemini API – Extracts structured data from the invoice image.
   3. Notion API – Stores the parsed transactions into a user’s private or shared workspace.
   4. QuickChart API – Renders charts based on the summarized spending data.
   
   💡 Final Thoughts  
   
   By combining low-code automation, AI, and chat-based UX, this workflow removes the boring work from budgeting. It’s a perfect demonstration of how accessible technologies like n8n can empower users to take smarter control of their finances—in real time, with minimal input.
   
   Whether you’re an individual tracking personal expenses or a freelancer monitoring costs, this workflow offers a scalable and intelligent solution to financial data management.
   
   Start snapping, start saving. 📸💸📊

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.