This article provides a complete, practical walkthrough of the Code Editimage 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:
   Prompt-Based Object Detection Using Google Gemini 2.0 and n8n
   
   Meta Description:
   Learn how to build a visual AI workflow in n8n using Google Gemini 2.0 for prompt-based object detection. This guide shows how to identify and annotate custom image subjects using bounding boxes.
   
   Keywords:
   n8n, Gemini 2.0, Google Gemini, object detection, image recognition, computer vision, prompt engineering, image annotation, AI automation, bounding boxes, multimodal AI, visual AI
   
   Third-Party APIs Used:
   
   1. Google Gemini (API Endpoint: https://generativelanguage.googleapis.com) – Used for multimodal, prompt-based object detection on images.
   
   —
   
   Article:
   
   Prompt-Based Object Detection with n8n and Google Gemini 2.0: A Visual AI Workflow
   
   The future of computer vision is prompt-based—and it's already here. With the release of Gemini 2.0, Google brought a new capability to AI-powered image recognition: multimodal input handling. Combine this with n8n’s powerful no-code automation capabilities, and you have yourself a fully operational object detection system that responds to natural language prompts. In this article, we walk you through a workflow that detects and highlights rabbits in a test image using bounding boxes.
   
   Use Case: Detecting Rabbits in An Image
   
   Traditional object detection pipelines rely on pre-trained models that can recognize a fixed set of classes. What if you want your AI assistant to look for context-specific or abstract objects like “rabbits”, “adults with children”, or even “cars parked illegally”? That’s exactly what Gemini 2.0 is designed for—and with this workflow, you can do just that.
   
   How It Works — Step-by-Step
   
   Let’s break down how this visual AI workflow is constructed using n8n and the Gemini 2.0 API:
   
   1. Download the Test Image
   
   The flow kicks off using a Manual Trigger node, followed by the HTTP Request node that downloads a test image from a given URL. In our demo, we use a photo of animals at a petting zoo.
   
   2. Extract Image Dimensions
   
   Before sending the image off for AI analysis, we use n8n’s built-in Edit Image node (in "information" mode) to extract the image’s width and height. This information is crucial because Gemini provides bounding box coordinates in a normalized 0–1000 scale. To place boxes correctly, we’ll need to rescale them to the actual image dimensions.
   
   3. Ask Gemini 2.0 to Detect Rabbits
   
   Next comes the magic. A second HTTP Request node makes a call to Google's Gemini 2.0 multimodal endpoint. The node sends a JSON payload comprising:
   
   - A natural language prompt: “I want to see all bounding boxes of rabbits in this image.”
   - The JPEG image embedded in base64 format.
   - An explicitly defined JSON schema requesting an array of bounding boxes containing box_2d (coordinates) and label.
   
   The use of prompt-based object detection allows us to tailor our detections to specific, contextual objects—which was not feasible with older general-purpose recognition APIs.
   
   4. Assign and Scale the Object Coordinates
   
   Once we receive Gemini’s response, a Set node extracts variables like the normalized coordinates, image width, and height. These are passed into a Code node where custom JavaScript scales each coordinate to align properly with the actual image dimensions.
   
   This rescaling step adjusts each box_2d component (xmin, ymin, xmax, ymax) from a 0–1000 scale to pixel-based coordinates based on individual image width and height.
   
   5. Draw Bounding Boxes
   
   Finally, the bounding boxes are drawn on the original image using another Edit Image node in "multiStep draw" mode. Each bounding box is rendered with a semi-transparent magenta color, making it visually clear where the AI believes rabbits are located.
   
   The image is now annotated and ready to be viewed or stored, showcasing Gemini’s object detection in real-world visuals.
   
   Why Not Use a Basic LLM Node?
   
   Interestingly, n8n’s Basic LLM node (LangChain) doesn't currently support Gemini 2.0’s multimodal input. That’s why the HTTP Request node is used here with a custom JSON payload to interact with the API directly.
   
   Key Benefits of This Workflow
   
   - Prompt-driven Vision AI: You can change the detection target simply by updating the text prompt.
   - Visual Output: Bounding boxes allow quick visual verification of detection accuracy.
   - Scalable: This can be extended to bulk image processing or integration into more complex applications like contextual search engines.
   - Customizable: Easily swap out the image, prompt, or even annotation style.
   
   Limitations & Observations
   
   In the test runs, Gemini 2.0 successfully drew boxes around five out of six rabbits visible in the image. While not perfect, this is an exciting preview of what's possible. As the model continues to mature, we can expect even higher accuracy for abstract or nuanced prompts.
   
   Conclusion
   
   This project demonstrates how n8n and Google Gemini can work together to build intelligent, prompt-based object detection automations without writing full-fledged web apps or backend services.
   
   With just a few well-configured nodes, you can instruct AI to locate practically anything visible in an image. It’s a whole new era for no-code image intelligence, and it’s just the beginning.
   
   👨‍💻 Want to Try It Yourself?
   
   Download this workflow from the n8n library, test with your own images (just ensure width and height are extractable), and enjoy AI bounding boxes that respond to your instructions—no deep learning experience necessary.
   
   Need help? Join the n8n Discord or check out the Community Forum for more workflow ideas and support from fellow automation enthusiasts.
   
   Happy hacking!
   
   — End —

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.