This article provides a complete, practical walkthrough of the Code Editimage Update 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:**  
   Automated Image Captioning with n8n and Google Gemini: An End-to-End Workflow for Visual Content Enhancement
   
   **Meta Description:**  
   Leverage n8n and Google’s Gemini multimodal AI to automatically generate and overlay image captions. This guide walks through an image captioning workflow that combines AI-generated text with on-image rendering for enhanced visual storytelling.
   
   **Keywords:**  
   n8n, Google Gemini, AI image captioning, automated captioning, LangChain, image processing, visual AI, multimodal AI, generate image descriptions, edit image n8n, workflow automation, image annotation
   
   **Third-Party APIs Used:**
   
   - Google Gemini API (PaLM via LangChain)
   - Pexels (via HTTP request to fetch sample image)
   
   ---
   
   ## Caption Your Images Automatically Using AI and n8n
   
   Creating compelling visual content requires more than just high-quality imagery—context can be just as important. In this guide, we demonstrate how to use n8n, a powerful workflow automation tool, in tandem with Google Gemini's AI capabilities to automatically generate captions for images and overlay them directly onto those images.
   
   Are you looking to streamline your image captioning workflow? Whether you're a content creator, social media marketer, or digital publisher, this innovative n8n setup will help you amplify your visuals with AI-generated captions that add valuable context and storytelling.
   
   Let’s dive into how this is achieved.
   
   ---
   
   ## Step 1: Import an Image Using HTTP Request
   
   The journey starts with importing an image using the HTTP Request node. For demonstration purposes, the workflow pulls a stock image from [Pexels](https://www.pexels.com/photo/photo-of-a-cat-1267338/), but in real-world applications, this could be replaced with uploads via webhook, file storage triggers, or client APIs.
   
   The image is then passed to two branches:
   
   - One for resizing the image to make it suitable for AI processing.
   - One for gathering metadata such as image dimensions, which we’ll later use to calculate optimal caption placement.
   
   ---
   
   ## Step 2: Resize and Send to Google Gemini for Caption Generation
   
   Utilizing n8n’s Resize Image node, the image is scaled to a manageable 512x512 resolution—ensuring compatibility and consistent input quality for the AI model.
   
   Next, we leverage the power of Google's Gemini via LangChain's ChainLLM node. This node allows multimodal input—meaning it can process text and image input.
   
   ### Gemini Prompt Strategy
   
   The system prompt guides the AI to follow an intelligent structure for image captioning:
   
   - Who is in the image?
   - When and where was it likely taken?
   - What’s happening in the image?
   - Add a punny title to enhance engagement.
   
   This prompt helps the AI generate human-like captions enriched with storytelling flair.
   
   A Structured Output Parser ensures consistency in the AI output format, returning a JSON object like:
   
   ```json
   {
     "caption_title": "Paws and Reflect",
     "caption_text": "A curious kitten lounges by the sill during a lazy afternoon in Brooklyn."
   }
   ```
   
   ---
   
   ## Step 3: Merge Image and Caption Metadata
   
   Using the Merge node, the image metadata and AI-generated caption are combined into a single payload, preparing it for rendering.
   
   This merger ensures both the visual and textual components are available for layout positioning and final rendering stages.
   
   ---
   
   ## Step 4: Calculate Caption Placement
   
   A Code node performs intelligent calculations to determine where the caption should appear:
   
   - It calculates the ideal font size based on image height.
   - Determines the number of text lines required.
   - Computes position offsets for both the caption background box and actual text.
   
   This makes sure the caption is not just dumped on the image but is thoughtfully embedded with optimal readability and minimal disruption to the image’s visual aesthetics.
   
   ---
   
   ## Step 5: Overlay the Caption on the Image
   
   The final step is the actual image editing, all done via n8n's Edit Image node using a multi-step operation:
   
   1. Draw a semi-transparent black rectangle at the bottom of the image to serve as the caption background.
   2. Overlay the caption text in white, using a true-type font (Arial) with the previously calculated font size and positioning.
   
   The final output is a richly annotated image with both visual and textual depth.
   
   ---
   
   ## Example Output
   
   ![](https://res.cloudinary.com/daglih2g8/image/upload/f_auto,q_auto/v1/n8n-workflows/l5xbb4ze4wyxwwefqmnc#full-width)
   
   *FIG 1. Example input image with AI-generated caption*
   
   ---
   
   ## Why This Workflow Matters
   
   This n8n workflow showcases the power of combining automation with modern AI APIs to enhance digital content. With no need to manually caption or edit images, users gain:
   
   - Time savings on repetitive content generation tasks
   - Improved accessibility through textual descriptions
   - A scalable solution for batch-processing large image datasets
   - Visually consistent content for marketing, social media, and documentation
   
   Whether you're building a custom CMS, a creative pipeline, or an internal tool, this workflow provides a modular framework that’s easily extendable.
   
   ---
   
   ## Try It & Extend It
   
   Want to get started? Just import the shared workflow into your n8n instance (self-hosted or cloud), plug in your Google Gemini API credentials, and hit "Execute Workflow".
   
   You can further customize:
   - Trigger from user uploads instead of static URLs
   - Store generated images in S3, Google Drive, or Dropbox
   - Add caption translation using additional AI nodes
   
   The possibilities are endless!
   
   ---
   
   ## Resources
   
   - n8n Documentation: [https://docs.n8n.io](https://docs.n8n.io)
   - Google Gemini API (PaLM): [https://ai.google.dev/](https://ai.google.dev/)
   - LangChain Integration with n8n: [n8n + LangChain](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainllm)
   - Join the n8n Community:  
     - Discord: https://discord.com/invite/XPKeKXeB7d  
     - Forums: https://community.n8n.io/
   
   ---
   
   With just a handful of nodes and the intelligence of multimodal AI, n8n turns a static image into dynamic content—all automatically.
   
   Happy automating!

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.