This article provides a complete, practical walkthrough of the Localfile Splitout Send 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 Rental Payment Reconciliation with n8n and AI: A Self-Hosted Workflow Guide
   
   Meta Description:
   Streamline your rental payment reconciliation process using this self-hosted n8n workflow. Leverage AI agents, local Excel files, and automated CSV parsing to detect missed or incorrect tenant payments quickly and securely.
   
   Keywords:
   n8n workflow, rental payment reconciliation, AI Agent, self-hosted automation, rent tracking, Excel automation, CSV parser, OpenAI GPT-4, tenant management, SheetJS, LangChain, local file trigger
   
   Third-Party APIs and Libraries Used:
   
   1. OpenAI GPT (via OpenAI Chat Model node)
      - Used to power the AI agent for rent payment analysis and decision-making.
      - Model: gpt-4o
   
   2. SheetJS (XLSX library)
      - Integrated within n8n’s Code node.
      - Used to read, update, and append data to locally-hosted Excel workbooks.
   
   3. LangChain
      - Integrated as part of the AI agent and tool nodes.
      - Enables advanced prompt execution, structured output parsing, and access to tenant/property data through reusable functions.
   
   —
   
   Article:
   
   Automating Rental Payment Reconciliation with n8n and AI: A Self-Hosted Workflow Guide
   
   Reconciling rent payments manually is time-consuming, error-prone, and often overwhelming—especially when managing multiple tenants across different properties. Fortunately, with the power of open-source automation platform n8n, it's now possible to streamline this tedious process using a self-hosted, privacy-preserving workflow powered by AI, local spreadsheets, and structured logic.
   
   In this article, we walk through a robust n8n workflow that automates the ingestion of bank statements, compares transactions against tenancy data stored in a local Excel file, and flags discrepancies such as missed or partial payments, late fees, and end-of-term balances. Let’s explore how the system works.
   
   Step 1: File Trigger for Incoming Bank Statements
   
   The workflow begins with the “Watch For Bank Statements” node—an n8n Local File Trigger that continuously monitors a specified directory (/home/node/host_mount/reconciliation_project) for new CSV files. This makes data ingestion seamless. As soon as a new bank statement is dropped into the folder, the workflow kicks off automatically.
   
   This approach keeps all local data secure without needing to connect to external services—a significant advantage for workflows handling financial and personally identifiable information (PII).
   
   Step 2: Parsing and Preparing Data
   
   Once a CSV bank statement is detected, it is read using the “Get Bank Statement File” and “Get CSV Data” nodes. The extracted transactions are structured in a common format, listing dates, reference codes, money in, and money out. This structured data becomes the input for the AI analysis that follows.
   
   Simultaneously, the workflow initializes by setting critical variables such as the path to a local reconciliation spreadsheet, which holds tenant and property data.
   
   Step 3: Delegating to an AI Agent for Analysis
   
   At the heart of the workflow lies the “Reconcile Rental Payments” node, which leverages an OpenAI GPT-4 model (via LangChain’s “OpenAI Chat Model” node). This smart AI agent analyzes transaction data against tenancy agreements using details obtained from two helper tools:
   
   - Get Tenant Details uses SheetJS to read tenant info such as rent amount, due dates, and notes from the “tenants” sheet of the local Excel file.
   - Get Property Details fetches the associated property data from the "properties" sheet.
   
   The AI agent uses these data points to answer a focused question: “What actions need to be taken based on this month’s bank statement?”
   
   Its output is structured in JSON, listing actionable items for each tenant, including:
   
   - Missed Rent
   - Incorrect Payment Amount
   - Late Payment (within tolerance or not)
   - Unpaid Fees Near Lease End
   
   A built-in output parser ensures standardized data formatting, ideal for downstream processing.
   
   Step 4: Generating an Actionable Report
   
   With reconciliation issues identified, the AI output is split into individual actions by the “Alert Actions To List” node and then handed over to the “Append To Spreadsheet” node.
   
   Using the powerful SheetJS library, this custom Code node appends each alert to the “alerts” tab of the same Excel workbook. Before writing, the system creates a backup (reconcilation-workbook.xlsx.bak) as a precaution—an important safeguard against accidental overwrites or data corruption.
   
   The report captures every relevant data point, including the tenant ID, name, property ID, postcode, nature of the issue, and the date. Over time, this builds a full historical record of flagged discrepancies—crucial for audits or tenant communications.
   
   Privacy-Focused and Ideal for Self-Hosting
   
   This n8n setup operates entirely in a self-hosted environment:
   
   - No data leaves your local machine.
   - Only trusted APIs are used (OpenAI for analysis, which can be swapped for a local LLM).
   - Reads and writes happen within your local file system.
   - Sensitive information never crosses the internet.
   
   This makes it a fantastic solution for property managers and accountants interested in automating routine checks without compromising tenant privacy or regulatory compliance.
   
   Conclusion
   
   With n8n’s low-code framework and powerful extensibility, automating rental payment reconciliation is no longer a dream. This workflow demonstrates how local file triggers, LLMs like GPT-4, and structured Excel data can work in harmony to produce smart, actionable insights—no spreadsheets or manual tracking required.
   
   Plus, the modular nature of the workflow means you can easily adapt it to handle commercial leases, billing cycles, or other types of financial transaction monitoring.
   
   Start automating today and eliminate the monthly reconciliation headaches once and for all.
   
   Need help getting started?
   
   Join the conversation on the official n8n Forum or jump into their vibrant Discord community!
   
   —
   
   Bonus Tip:
   Always test workflows on sample data and maintain backups, particularly when performing write operations on important files. As noted in the workflow: “With code comes great responsibility!”
   
   Happy automating!

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.