This article provides a complete, practical walkthrough of the Splitout Extractfromfile Automation 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 Personal Finance: How to Extract and Log Spending Details Using n8n, Gmail, and AI
   
   Meta Description:
   Learn how an automated n8n workflow uses Gmail, AI models like Google Gemini and Groq, and Google Sheets to extract and structure personal financial transactions from emails and invoices into a centralized spreadsheet.
   
   Keywords:
   n8n, Gmail automation, Google Gemini, Groq AI, finance automation, personal bookkeeping, Gmail invoice parser, Google Sheets automation, expense tracking, LangChain, PDF extraction, structured output parser, AI email parser
   
   Third-Party APIs and Services Used:
   
   1. Gmail API (via OAuth2) – To access and read labeled emails with attached invoices and payment notifications.
   2. Google Gemini (Gemini 1.5 Flash via PaLM API) – Used to extract and structure transaction details using large language models.
   3. Groq (LLaMA 3.2 via Groq API) – Alternative LLM integration for parsing spend content using AI.
   4. Google Sheets API (OAuth2 for G Suite) – Used to log extracted data into predefined columns in an online spreadsheet.
   5. LangChain Nodes – Incorporates AI tools like chains, chat models, and structured output parsers to interact with LLMs effectively.
   
   —
   
   Article:
   
   Automated Expense Management Made Simple with n8n, Gmail, and AI
   
   Managing personal or business finances can quickly become overwhelming—especially when your inbox is flooded with invoices, card statements, and assorted transaction emails. Fortunately, with modern workflow automation tools like n8n and increasingly powerful language models (LLMs), you can automatically convert this clutter into clean, structured financial records. One such elegant solution is the “Extract spend details” n8n workflow, designed to read, parse, and log transaction data directly from your Gmail account into Google Sheets.
   
   Let’s walk through how this workflow operates and the technologies that power it.
   
   Step 1: Collecting Transactional Emails via Gmail API
   
   The workflow begins by monitoring your Gmail account using two dedicated Gmail Trigger nodes in n8n:
   
   - Get Invoice
   - Get Payment
   
   These nodes are configured with Gmail OAuth2 credentials to access the inbox (specifically labeled messages) and download email attachments—usually PDF invoicing files or payment confirmation messages. The automation checks for new emails every minute, ensuring timely data ingestion.
   
   Each email is labeled using Gmail filters—for example, invoices might go under one label and payment notifications under another. This initial step ensures the source emails are well-organized for automated processing.
   
   Step 2: Extracting Data from Attachments
   
   Upon detecting an email, n8n retrieves and attempts to extract content from attached PDF files using its built-in PDF extraction nodes (Extract invoice and Extract payment). These nodes have password-protected support, which is often necessary for bank or vendor-issued invoices. They parse text content out of the PDF's binary data into structured JSON format.
   
   Step 3: Separating and Classifying Email Types
   
   Next, a powerful Switch node evaluates numerous conditions to classify incoming emails. Based on the sender’s email address, it identifies whether the message:
   
   - Contains multiple transactions (e.g., daily credit card summaries)
   - Contains a single transaction (e.g., instant notification for one purchase)
   - Is an invoice or receipt
   
   For instance, messages from service@pxbillrc01.cathaybk.com.tw are marked as containing multiple records in one email, while single-spend notifications might come from noreply@messaging.hsbc.com.tw or hello@crypto.com. This classification is key to determining how the downstream logic should parse the content.
   
   Step 4: Parsing the Content with AI Language Models
   
   Here’s where the magic of AI comes in. Depending on the classification, the email content is passed into one of two AI language models integrated via LangChain support in n8n:
   - Google Gemini (via the PaLM API)
   - Groq’s LLaMA 3.2 model
   
   Using carefully crafted prompts, these models are asked to analyze the email content and return structured transaction details—this includes:
   - Transaction date
   - Merchant or service name
   - Transaction details
   - Amount
   - Category (Entertainment, Food & Beverage, Transportation, etc.)
   - Currency
   - Credit card used for the purchase
   
   The structured responses from the LLMs are then parsed and validated using LangChain’s Structured Output Parser nodes. The schema has been manually defined to ensure compliance with accounting standards and consistency in output formatting.
   
   Step 5: Storing Clean Data in Google Sheets
   
   Finally, the parsed and validated data is sent to a Google Sheet titled “raw data 2” via the Google Sheets API. Within the n8n workflow, fields like date, service, amount, payment method, and category are cleanly mapped into corresponding columns.
   
   This structured spreadsheet acts as a real-time ledger, continuously updated as new emails arrive.
   
   Why This Workflow Matters
   
   ✔️ Accuracy at Scale  
   Parsing receipts or invoices manually leaves significant room for human error. Leveraging AI tools ensures that even unstructured content gets interpreted flawlessly.
   
   ✔️ Time Savings  
   Monitoring every transactional email and entering data manually is a drain on resources. With this setup, a hands-off process runs silently in the background—every minute.
   
   ✔️ Extensibility  
   The use of open-source automation (n8n) plus modular LLM tools means the stack can be expanded—add more banking sources, feed data into accounting software, or trigger alerts for unusual transactions.
   
   ✔️ Personalization  
   With toolkit elements like sticky notes and customizable schemas/prompts, users can tailor the behavior to their email format, preferred languages, or financial categories.
   
   Conclusion
   
   The “Extract spend details” template is a prime example of how AI-enhanced automation can revolutionize previously tedious workflows. By integrating Gmail, Google Sheets, and LLMs like Google Gemini and Groq directly into n8n, managing personal and business expenses becomes not only manageable but efficient and intelligent.
   
   Whether you're a freelancer, a budget-savvy professional, or a bookkeeper looking for digital tools that do more, this workflow provides both inspiration and practicality.
   
   —
   
   Ready to install or adapt this workflow? Head over to your n8n instance, import the template, and start transforming your inbox into a well-organized expense manager.

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.