This article provides a complete, practical walkthrough of the Splitout Converttofile Automation Webhook 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 Image Generation & Storage with OpenAI, Google Drive, and Google Sheets Using n8n
   
   **Meta Description:**  
   Learn how to create an automated workflow in n8n that uses OpenAI’s DALL·E model for image generation, uploads results to Google Drive, and logs metadata into Google Sheets for seamless record-keeping.
   
   **Keywords:**  
   n8n, OpenAI, DALL·E, Workflow automation, Google Drive API, Google Sheets API, AI-generated images, automation, ChatGPT image workflow, no-code automation, image prompt automation
   
   **Third-Party APIs Used:**
   
   1. OpenAI API (DALL·E Model)
   2. Google Drive API
   3. Google Sheets API
   
   ---
   
   **Article:**
   
   # Automating AI Image Generation and Storage with n8n, OpenAI, and Google Services
   
   In the age of generative AI, the ability to streamline content creation workflows can significantly boost productivity and reduce manual effort. With tools like OpenAI’s image-generating models and powerful no-code platforms like n8n, it's easier than ever to build custom automation without writing complex code.
   
   This article walks you through a complete automation workflow built in n8n, enabling users to generate images using a prompt via chat, store those images in Google Drive, and log essential metadata—such as image links, thumbnails, and costs—into a Google Sheets document.
   
   ## Overview of the Workflow
   
   The n8n workflow titled “template-demo-chatgpt-image-1-with-drive-and-sheet” integrates multiple systems into a seamless pipeline. Here’s a high-level summary:
   
   1. A chat trigger receives an image prompt.
   2. The prompt is sent to OpenAI’s image generation endpoint.
   3. The returned image is processed and saved to Google Drive.
   4. The image details (such as links and thumbnail) are saved in one Google Sheet.
   5. OpenAI usage and cost details are logged into another Google Sheet.
   
   Let’s break it down further.
   
   ---
   
   ## Step 1: Triggering the Workflow via Chat
   
   The flow begins when a user submits a message using a chat system. The node "When chat message received" captures the `chatInput`—which is the image prompt text. This node acts as a webhook that initiates the automation.
   
   ---
   
   ## Step 2: Generating Images with OpenAI
   
   The captured prompt is passed to the “HTTP Request” node, which calls OpenAI's image generation endpoint:  
   `https://api.openai.com/v1/images/generations`
   
   The request uses these parameters:
   - `model`: gpt-image-1
   - `prompt`: user's chat input
   - `output_format`: jpeg
   - `quality`: low (for faster output and reduced cost)
   - `size`: 1024x1024 pixels
   - `n`: 1 image
   - `moderation`: low
   
   The API responds with a base64-encoded image in the `b64_json` field along with metadata like the creation timestamp and usage statistics.
   
   ---
   
   ## Step 3: Processing and Uploading the Image
   
   The returned image is stored in a data array and processed in a loop (using “Split Out” and “Loop Over Items”).
   
   Within each loop cycle:
   
   1. A filename is assigned using the current timestamp ("Edit Fields-file_name").
   2. The base64 data is converted to a binary file (“Convert to File”).
   3. The file is uploaded to a specified folder in Google Drive via the “Google Drive” node.
   
   Once the file is uploaded, Google Drive returns metadata such as:
   - File ID
   - Web view link
   - Thumbnail link
   
   This metadata is stored for later use.
   
   ---
   
   ## Step 4: Recording Image Metadata in Google Sheets
   
   The metadata is compiled in the “Edit Fields1” node before being passed to the “Google Sheets” node.
   
   Each row added to the sheet includes:
   - The original prompt
   - A clickable link to the image (webViewLink)
   - A thumbnail (embedded using the `IMAGE()` function in Sheets)
   
   This ensures each image is properly documented and easily viewable from within the spreadsheet.
   
   ---
   
   ## Step 5: Logging API Usage and Costs
   
   OpenAI's response includes usage metrics like input and output tokens. These values are passed to the “Aggregate” node and then into “Google Sheets1,” which logs the data in another worksheet labeled “usage.”
   
   The sheet tracks:
   - Prompt
   - Timestamp
   - Input and output tokens
   - Estimated costs:
     - Input: $0.00001 per token
     - Output: $0.00004 per token
     - Total estimated cost
   
   This allows for real-time monitoring of API usage and helps manage AI resource budgets effectively.
   
   ---
   
   ## Benefits of This Workflow
   
   - 🧠 Uses AI to create unique, prompt-based images instantly  
   - ☁️ Automatically stores files in the cloud (Google Drive)  
   - 🧾 Maintains a structured record with thumbnails and prompt metadata  
   - 💰 Tracks usage cost transparently  
   - ⚙️ Completely automated and scalable with little intervention  
   
   ---
   
   ## Conclusion
   
   This workflow highlights the power of combining AI technologies like OpenAI’s models with no-code automation tools like n8n. By integrating Google Drive and Google Sheets, you build not just a content creation pipeline but a full documentation and cost-tracking system.
   
   Whether you’re a creative professional, a marketing team experimenting with AI content, or a developer building prototypes, this setup can save hours of work and assist in managing digital assets effectively.
   
   Interested in trying it? You can recreate this workflow in your own n8n instance using the nodes discussed above—or modify it to suit your own automation goals.
   
   ---
   
   Created by @darrell_tw_  
   👨‍💻 AI & Automation Engineer  
   📎 Connect via: [X](https://x.com/darrell_tw_) | [Threads](https://www.threads.net/@darrell_tw_) | [Instagram](https://www.instagram.com/darrell_tw_/) | [Website](https://www.darrelltw.com/)

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.