This article provides a complete, practical walkthrough of the Splitout Editimage Automate 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:  
   Automated Passport Photo Validation Using AI Vision and n8n
   
   Meta Description:  
   Discover how to build an automated passport photo validator using n8n workflows, Google Drive, and AI vision models like Google Gemini. A streamlined pipeline for verifying photo compliance with UK passport standards.
   
   Keywords:  
   n8n, Passport Photo Validator, AI Vision, Google Drive, Google Gemini, OpenAI, Image Processing Workflow, UK Passport Photo Guidelines, Workflow Automation, LangChain, Image Recognition
   
   Third-Party APIs Used:  
   
   - Google Drive API (for downloading images)
   - Google Gemini (models/gemini-1.5-pro-latest via PaLM API)
   - LangChain (AI Output Parsers and LLM Chains)
   
   —
   
   Article:
   
   Automated Passport Photo Validation Using AI Vision and n8n
   
   In a world increasingly run by automation and AI, repetitive and rule-based tasks are ripe for innovation—and passport photo validation is a prime example. Verifying whether a digital portrait complies with government-issued requirements can be tedious, especially when processing large numbers of submissions. Fortunately, by using tools like n8n, you can automate this process with AI-powered image analysis.
   
   In this article, we’ll walk through a sample workflow built in n8n for validating passport photos according to the UK Government's guidelines. We'll make use of Google Drive for input, resize images automatically, and pass them through Google Gemini’s advanced vision model to determine whether each image meets the stringent criteria. Let's explore how this workflow functions and the services powering it.
   
   📸 Step 1: Import Photos Using Google Drive
   
   The first step in the workflow starts with the Manual Trigger node in n8n, designed for testing purposes. Once activated, the workflow uses a Set node to list a group of predefined image URLs hosted on Google Drive. These portrait URLs are then split into individual data entries using a SplitOut node, allowing each image to pass through the automation independently.
   
   🔄 Step 2: Download and Preprocess the Images
   
   Each image is then downloaded directly from Google Drive via the Google Drive node. To ensure compatibility with the constraints set by AI vision models and the UK passport requirements (minimum 600x750 pixels, clarity, etc.), n8n resizes each image to 1024x1024 pixels using the "Resize For AI" node. The resize operation preserves quality while preparing the photo for further processing by the AI.
   
   🤖 Step 3: Analyze the Image Using Google Gemini
   
   Next, resized images are passed into a powerful AI image analysis model—Google Gemini 1.5 Pro via the Google PaLM API. This is orchestrated through LangChain's LLM chain node, which structures the prompt for the AI using real-world rules obtained directly from the UK government’s passport photo guidelines.
   
   Here are some key criteria that are verified:
   
   - No red-eye, shadows, or other people in the photo  
   - A plain light-colored background  
   - The subject facing forward, with a neutral expression  
   - No hair covering the eyes or face obstructions  
   - Glasses allowed only under specific conditions  
   - Baby and child-specific rules such as no visible support
   
   These criteria are passed into Gemini dynamically, alongside the binary image input. The consistent and structured prompt ensures that the AI considers every essential factor reliably.
   
   🧠 Step 4: Structured AI Output for Easy Integration
   
   The AI model’s response is parsed using LangChain’s Structured Output Parser node. The parsed JSON object includes:
   
   - is_valid – A boolean flag indicating compliance  
   - photo_description – A textual description of the image contents  
   - reasons – An array explaining why the photo may not be valid
   
   The structured output makes the data ready for consumption by your database, CRM, or external backend, enabling seamless integration into larger systems or workflows.
   
   🧪 Test and Extend the Workflow
   
   The final product of this workflow is a powerful, fully-automated AI-powered photo validation system that can be adapted for countless use cases—from validating employee badge photos to streamlining visa applications.
   
   And since it's built with n8n's modular, node-based interface, it's highly customizable:
   
   - Swap out the image source from Google Drive to Dropbox, S3, or even webhooks  
   - Use OpenAI's GPT-4 Vision model instead of Gemini (or any other compatible model)  
   - Add conditional branches to notify users of invalid submissions  
   - Automatically save results to Airtable, Google Sheets, or a SQL database  
   
   This combination of n8n's automation prowess and the intelligence of modern vision models serves as a powerful example of how AI can handle human-centered tasks with speed and accuracy.
   
   🎓 Final Thoughts
   
   By transforming passport photo validation into an automated workflow, we reduce manual intervention, increase throughput, and improve consistency. This solution is also perfectly scalable—from a developer experimenting with a few images to government agencies processing thousands daily.
   
   Whether you're looking to build AI tools that analyze images, automate business tasks, or streamline compliance, this blueprint demonstrates the potential of combining low-code workflows with next-gen machine learning models.
   
   Ready to try it out? Join the n8n community, explore more documentation, and take your first step into AI-powered automation.
   
   —
   
   Need help getting started or customizing the workflow? Join the n8n Discord or post your questions in the n8n Forum.

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.