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 Sample Data Generation with GPT-4 and n8n: Create CSV Files Featuring Fictional Character Profiles
   
   Meta Description:
   Discover how to use GPT-4 within an n8n workflow to generate JSON-based sample data of fictional users and convert it into clean CSV files—complete with UTF-8 encoding fixes and file-saving automation.
   
   Keywords:
   n8n, GPT-4, OpenAI, CSV automation, JSON to CSV, no-code automation, data generation, fictional user data, OpenAI GPT, export to CSV, sample data creation
   
   Third-Party APIs Used:
   - OpenAI (GPT-4) — via OpenAI API for natural language-based content generation
   
   —
   
   Article:
   
   Create CSV Files with Fictional User Data Using GPT-4 and n8n
   
   In software development and automation environments, there’s often a need for mock data—whether for database testing, UI demos, or data pipeline validation. While developers can manually create data or use static scripts, automating the process makes it faster, scalable, and more flexible. With n8n—a powerful workflow automation tool—and GPT-4, we can generate fictional user profiles dynamically and save them as CSV files ready for use.
   
   In this article, we’ll take a deep dive into an n8n workflow that uses OpenAI’s GPT-4 model to create sample user data, process it into CSV format, and save it to disk—all with automation and precision.
   
   🧠 Overview of the Workflow
   
   This n8n workflow, aptly named “Prepare CSV files with GPT-4”, performs the following key steps:
   
   - Triggers manually when needed
   - Uses GPT-4 to generate random fictional character-based users
   - Parses, processes, and formats the data into CSV
   - Cleans up encoding issues (e.g., UTF-8 BOM bytes)
   - Saves CSV files to your local file system
   
   Let’s break down what’s happening under the hood.
   
   🚀 Step-by-Step Breakdown
   
   1. Manual Trigger (“Execute Workflow” Node)
   The process begins with a Manual Trigger node. Running this node kicks off the generation cycle. This means the workflow is intended to be executed when needed, rather than on a schedule or webhook.
   
   2. Generate User Data with GPT-4 (OpenAI Chat Node)
   The second node leverages OpenAI’s GPT-4 model to generate a list of fictional users. The request includes detailed instructions:
   
   - Generate 10 random fictional characters using alliterative names (e.g., “Frodo Fenton”).
   - Include fields like user_name, user_email, subscribed status, and date_subscribed.
   - Sheer creativity is requested from GPT-4, but the structure is precise: return only JSON, ensure date_subscribed values are no later than October 1, 2023, and omit that field for unsubscribed users.
   
   To add variety, the workflow makes three such calls to GPT-4, collecting three unique datasets of fictional users.
   
   3. Split JSON Results into Batches (Split in Batches Node)
   Each dataset is separated into individual batches for easier file creation and isolated processing. Batch size is set to 1 so each response from GPT-4 spawns its own CSV file.
   
   4. Parse JSON Structure (Set Node)
   The stringified JSON arrays from GPT-4’s response are parsed into real JSON arrays using JavaScript's JSON.parse. This ensures all user objects are usable in later workflow steps.
   
   5. Flatten JSON Arrays into Items (Item Lists Node)
   Because n8n works elegantly with lists of items, this node breaks down the JSON array so each user becomes a separate item. This is essential for conversion to tabular data formats like CSV.
   
   6. Convert JSON Items to CSV (Spreadsheet File Node)
   With the cleaned and structured data, we now convert the user list into CSV. Each generated file is named dynamically—e.g., funny_names_1.csv, funny_names_2.csv, etc.—and includes a header row.
   
   7. BOM Byte Fix (Move Binary Data & Encoding Options)
   One of the subtle yet essential steps involves handling BOM (Byte Order Mark) issues common with UTF-8 files. If not handled, some spreadsheet tools or systems might misinterpret the file format.
   
   Two nodes are used to:
   - Strip unnecessary BOM bytes from the beginning of the file
   - Convert the sanitized JSON data to a proper binary format for file writing
   
   This ensures your CSV files are opened correctly across systems, including Excel or Unix-based CLI tools.
   
   8. Save CSV to Disk (Write Binary File Node)
   Finally, the polished CSV files are saved to disk in the designated directory (by default: ./.n8n/). These are now ready for import, sharing, testing, or demos.
   
   🎁 Example Output
   
   Here’s a sample of one user record generated by GPT-4:
   
   {
     "user_name": "Bilbo Baggins",
     "user_email": "bilbobaggins@gmail.com",
     "subscribed": true,
     "date_subscribed": "2023-03-12"
   }
   
   The workflow packages 10 such records into a CSV with consistent formatting. Here's the resulting CSV format:
   
   | user_name       | user_email               | subscribed | date_subscribed |
   |-----------------|--------------------------|------------|-----------------|
   | Bilbo Baggins   | bilbobaggins@gmail.com   | true       | 2023-03-12      |
   | Frodo Fawkes    | frodo.fawks01@gmail.com  | false      |                 |
   
   📦 Why This Workflow Matters
   
   This approach to automated mock data generation using GPT-4 is highly adaptive and efficient. It eliminates time spent writing dummy records manually while promoting high-quality, contextually realistic example data.
   
   Pairing this with n8n’s versatile automation engine also means you can easily extend the workflow to:
   - Email users the generated CSV
   - Upload to cloud storage (Dropbox, Google Drive)
   - Feed into a test database
   - Schedule periodic generation for testing environments
   
   🛠️ Extending the Workflow
   
   Want to build on this? Here are a few ideas:
   
   - Add a webhook instead of manual trigger to integrate with external systems
   - Parameterize the prompt to generate different types of sample data (e.g., products, reviews)
   - Send a Slack notification upon completion using n8n’s Slack nodes
   - Store generated files in remote servers or cloud storage
   
   🔍 Final Thoughts
   
   Combining the creative power of GPT-4 with the no-code flexibility of n8n offers unparalleled potential for automation enthusiasts and developers alike. This workflow is a simple yet powerful foundation for automating repetitive tasks, enriching test environments, and importing realistic data into your projects.
   
   Whether you're building demo apps, onboarding flows, test scripts, or simply need a CSV full of Harrys and Frodoes—you now have a slick, automated way to make it happen.
   
   —
   
   For more workflows and custom automations, dive into the n8n community or explore templates to bring your ideas to life.

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.