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:
   How to Generate and Overlay AI Captions on Images Using n8n and Google Gemini
   
   Meta Description:
   Learn how to build an automated workflow in n8n that uses Google’s Gemini multimodal model to generate AI-powered image captions and overlay them onto photos using built-in image-editing capabilities.
   
   Keywords:
   n8n workflow, AI image captioning, Google Gemini API, overlay text on image, automated image processing, edit image with AI, multimodal LLM, generate image captions, OpenAI alternative, image annotation automation
   
   Third-party APIs Used:
   1. Google Gemini (PaLM) API
   2. Pexels (image source)
   
   ---
   
   Article:
   
   # How to Generate and Overlay AI Captions on Images Using n8n and Google Gemini
   
   Artificial Intelligence is rapidly transforming content creation, and one exciting use case is generating descriptive captions for images. With n8n—a powerful workflow automation tool—you can build a fully automated pipeline that uses Google Gemini's vision model to produce smart, contextual image captions and overlay them neatly onto your images.
   
   This article walks you through a real-world implementation of such a workflow using n8n’s visual node-based interface. Whether you want to produce visually-rich journalistic content, add watermarks or copyrights, or just have some fun with AI and photography, this guide shows you how to bring it all together.
   
   ## Overview of the Workflow
   
   This workflow does three key things:
   1. Imports an image from a remote URL
   2. Uses Google Gemini's multimodal vision model to generate a caption
   3. Calculates dynamic placement and overlays the caption onto the image
   
   Let’s break down each part.
   
   ---
   
   ## Step 1: Importing an Image
   
   The workflow is triggered manually with a Manual Trigger node and downloads an image using the HTTP Request node. In this example, a stunning image from Pexels is used:
   
   ```plaintext
   https://images.pexels.com/photos/1267338/pexels-photo-1267338.jpeg?auto=compress&cs=tinysrgb&w=600
   ```
   
   The image is fetched as a binary and passed downstream for processing. This can easily be replaced with a webhook or another type of trigger in your custom workflows.
   
   > 🎨 Node Used: `HTTP Request`
   
   ---
   
   ## Step 2: Prepare and Generate an AI-Powered Caption
   
   ### a. Resize Image for LLM Processing
   Models like Google Gemini work best with square and smaller resolutions to speed up inference. A resize step ensures the image dimensions are adjusted to 512x512 pixels.
   
   > 🔧 Node Used: `Edit Image (resize operation)`
   
   ### b. Use Google Gemini to Generate a Caption
   Multimodal support in n8n enables you to pass the image directly into a language model node and ask for an image description. By defining a chain through the `Image Captioning Agent` node, we input a prompt instructing the model to generate a creative caption.
   
   The Gemini model returns structured data—thanks to a Formal Output Parser—that includes two elements:
   - Caption Title (punny heading)
   - Caption Text (descriptive copy)
   
   Example:
   ```json
   {
     "caption_title": "Sands of Time",
     "caption_text": "A lone camel caravan ambles across the golden dunes at sunset, capturing a timeless desert voyage."
   }
   ```
   
   > 🤖 Nodes Used:
   > - `@n8n/n8n-nodes-langchain.lmChatGoogleGemini`
   > - `@n8n/n8n-nodes-langchain.chainLlm`
   > - `@n8n/n8n-nodes-langchain.outputParserStructured`
   
   ---
   
   ## Step 3: Overlay the Caption on the Image
   
   ### a. Image Metadata & Text Positioning
   Before overlaying the caption, a `Get Info` node grabs the image dimensions, which are later used in a custom `Code` node to calculate font size, text wrapping, and padding position. This ensures the text doesn’t overflow or appear awkwardly placed.
   
   > 💡 Code logic includes:
   - Estimating lines occupied by the caption
   - Calculating X/Y coordinates for the text and background
   - Dynamic font sizing
   
   > 🧠 Node Used: `Code`
   
   ### b. Merge and Add the Caption
   Two Merge nodes combine the structured caption data with the original image and the calculated drawing coordinates. Then, a final `Edit Image` node overlays both the translucent rectangle (aesthetically pleasing background) and the caption text atop the image.
   
   The caption appears toward the bottom of the image, with an opaque black box for contrast and stylish white text in Arial font for readability.
   
   > 🖋️ Node Used: `Edit Image (multiStep operation with draw & text)`
   
   ---
   
   ## Example Output
   
   Here’s what the output might look like—an image with a caption like:
   
   **Title:** “Sands of Time”  
   **Text:** “A lone camel caravan ambles across the golden dunes at sunset, capturing a timeless desert voyage.”
   
   ![Example Output](https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc)
   
   ---
   
   ## Why Use This Workflow?
   
   This example highlights the power of combining n8n’s automation flexibility with cutting-edge AI models like Google Gemini. Potential use cases include:
   
   - Automating social media visuals with auto-captions
   - Creating educational image infographics
   - Annotating product images for e-commerce
   - Watermarking using AI-generated context
   - Producing accessibility-enhanced content with descriptive alt text
   
   ---
   
   ## Final Thoughts
   
   This workflow showcases how easy it is to bring multimodal AI into content workflows using no-code/low-code tools. By integrating Google Gemini into n8n, you unlock a smart, scalable way to generate captions and edit images directly within your data workflows.
   
   Best of all, it’s modular! You can swap in different models (like OpenAI or Claude), use images from your CMS or Dropbox, or even caption images in bulk.
   
   Want to try it yourself? Head over to [n8n.io](https://n8n.io) and get started today!
   
   ---
   
   Need help? Join the vibrant n8n community in our [Discord](https://discord.com/invite/XPKeKXeB7d) or ask questions in the [Forum](https://community.n8n.io/)!

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.