This article provides a complete, practical walkthrough of the Manual Openai 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 Mock Data Generation and CSV Creation Using GPT-4 and n8n
   
   Meta Description:
   Discover how to use GPT-4 and n8n to automate the generation of mock user data, convert it into structured JSON, and export it as clean CSV files—all automatically and ready for testing or synthetic data needs.
   
   Keywords:
   n8n, GPT-4, OpenAI, automation workflow, mock data generation, CSV export, JSON processing, no-code automation, data pipeline, OpenAI API, create CSV files, batch processing, n8n workflow, synthetic data
   
   Third-Party APIs Used:
   - OpenAI API (GPT-4)
   
   Article:
   
   Creating Clean Synthetic CSV Files Using GPT-4 and n8n
   
   Whether you're developing a new application or testing a marketing automation flow, having realistic mock data is an essential part of the process. But typing out 50 fictional usernames and email addresses manually? That's a productivity nightmare. Thanks to the powerful combination of n8n and OpenAI's GPT-4 API, automating this task is not only possible, but surprisingly simple and effective.
   
   In this article, we'll walk through a no-code/low-code automation workflow built using n8n that leverages GPT-4 for generating mock user data and outputs three structured CSV files ready for use—cleaned, encoded properly, and saved to disk.
   
   Let’s step through the key components of this workflow and what makes it tick.
   
   🌟 Workflow Overview
   
   The workflow, aptly titled "Prepare CSV files with GPT-4", is triggered manually and automates the following steps:
   
   1. Generate mock user data using GPT-4.
   2. Parse and batch process that data.
   3. Convert each batch to CSV format.
   4. Handle encoding issues (by stripping UTF BOM).
   5. Save the CSV files locally.
   
   Best part? It runs on a button click.
   
   🧠 Step-by-Step Breakdown
   
   1. Triggering the Workflow
   
   Everything starts with a Manual Trigger node. This is perfect for workflows that you want to run on-demand—especially when testing or generating mock content.
   
   2. Generating Mock Data with GPT-4
   
   The next node connects to GPT-4 via the OpenAI API. It sends a carefully crafted prompt asking the model to return a JSON array of 10 fictional users.
   
   Here are the instructions baked into the prompt:
   - Full names must use alliteration (e.g., Harry Holmes).
   - Emails follow a standard format.
   - The “subscribed” boolean determines whether “date_subscribed” is included.
   - If “subscribed” is true, assign a randomized subscription date before October 1, 2023.
   - All data must be returned in a single-line JSON array—ideal for fast parsing.
   
   The workflow is configured to request 3 separate responses, effectively creating 30 users split across three JSON payloads.
   
   3. Splitting Into Batches
   
   The 3 JSON arrays are passed to the Split In Batches node. Each batch consists of a single output (one GPT response), facilitating isolated processing of each group of 10 users.
   
   4. Parsing JSON and Structuring Data
   
   Inside each batch, the Parse JSON step ensures the single-line response is converted into a true JSON array that n8n can understand and manipulate. The subsequent node, Make JSON Table, flattens the array so each user becomes an individual item.
   
   5. Generating CSV Files
   
   With clean JSON items in hand, the Convert to CSV node takes over. It names each file dynamically based on the batch index, creating filenames like funny_names_1.csv, funny_names_2.csv, and so on.
   
   6. Handling Encoding (Why BOM Matters)
   
   CSV files that include a UTF-8 Byte Order Mark (BOM) at the beginning can cause issues when read by certain platforms. Two nodes—Strip UTF BOM bytes and Create valid binary—handle this by cleaning and restructuring the binary data appropriately, ensuring compatibility and clean encoding.
   
   7. Saving Files to Disk
   
   Finally, the Save to Disk node writes each of the three CSV files to the local .n8n data directory. The files are ready to be used in test environments, simulations, or even mock marketing campaigns.
   
   📦 A Word on Output
   
   Here’s a brief snippet of what’s inside these CSV files:
   
   | user_name       | user_email               | subscribed | date_subscribed |
   |----------------|--------------------------|------------|-----------------|
   | Harry Holmes   | harryholmes@gmail.com     | true       | 2022-08-15      |
   | Frodo Fawkes   | frodo.fawks01@gmail.com   | false      |                 |
   | Luke Longbottom| lukeLongbottom@gmail.com  | true       | 2023-09-25      |
   
   🛠️ Tools & Dependencies
   
   The magic of this workflow depends on just one external API—the OpenAI API—utilized through the n8n “OpenAI” node. Everything else is native functionality within the n8n framework, including:
   - JSON parsing
   - Batch processing
   - CSV file creation
   - Encoding cleanup
   - File writing
   
   This means you can run the process many times a day with minimal cost and almost zero manual effort.
   
   💡 Customize It Further
   
   Want to tailor the mock dataset to your needs? You can easily adjust the GPT-4 prompt to include extra fields like “location,” “user_id,” or “company”. Similarly, increase the number of GPT completions to generate more files automatically.
   
   You can also:
   - Zip the CSV files for download
   - Email the files using n8n’s built-in SMTP nodes
   - Upload the files to S3 or Google Drive
   
   🚀 Wrapping Up
   
   This n8n workflow showcases how easy it can be to build a dynamic, AI-powered data generation and processing system. By combining GPT-4's immense generative power with the flexibility of n8n, you can create automated solutions that otherwise would take hours or even days to implement manually.
   
   Perfect for data prototyping, test automation, or learning how to blend AI into workflows—this setup is a glimpse into the low-code future of productivity.
   
   Now go forth and automate responsibly!
   
   — ✨ Powered by n8n + GPT-4 ✨ —

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.