This article provides a complete, practical walkthrough of the Http Executeworkflow 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:
   Building an Image Classifier for Land Use Detection with n8n, Qdrant, and Voyage AI
   
   Meta Description:
   Learn how to classify satellite images by land use types using a no-code KNN classifier built in n8n. This guide walks you through embedding images, querying a vector database, handling classification ties, and returning results with impressive 93%+ accuracy.
   
   Keywords:
   n8n image classifier, KNN classifier workflow, Qdrant vector search, Voyage AI embeddings, satellite image classification, land use detection, multimodal embedding, image recognition, machine learning workflow, no-code ML
   
   Third-Party APIs Used:
   
   - Voyage AI API (https://api.voyageai.com): for generating multimodal image embeddings.
   - Qdrant Cloud API (https://qdrant.tech): for vector similarity search and nearest neighbor querying using pre-uploaded image vectors and labels.
   
   Article:
   
   —
   
   In the evolving world of machine learning and automation, building an end-to-end image classification pipeline without writing a single line of code on the frontend is now possible. This article showcases a powerful example built using n8n — an open-source, low-code automation platform — that classifies satellite images of land using a K-Nearest Neighbors (KNN) classifier powered by third-party APIs like Voyage AI and Qdrant Cloud.
   
   Whether you're looking to explore computer vision approaches, automate satellite image detection, or simply learn about integrating modern AI APIs in scalable workflows, this walk-through provides a production-ready example using open datasets and enterprise-ready APIs.
   
   🛰️ Use Case: Classifying Land Types in Satellite Imagery
   
   The primary aim of this classifier is to detect the type of landscape appearing in a given satellite image. Types include forests, runways, beaches, residential areas, and more — 21 land categories in total.
   
   The dataset used was derived from the lands dataset available on Kaggle (Land Use Scene Classification), but this system can easily be adapted to any image classification dataset by updating the Qdrant collection accordingly.
   
   —
   
   📸 Architecture Overview
   
   This classifier is triggered whenever a URL of a new satellite image is submitted. Here's the breakdown of the automation workflow:
   
   1. 🎯 Input Image Reception  
      The process begins with an “Execute Workflow Trigger” node where a user provides the URL of a satellite image.
   
   2. 🧠 Generate Embedding (Voyage AI)  
      The image URL is passed to the Voyage API (using voyage-multimodal-3 model), which returns a vector embedding of the image. This embedding captures the semantic features of the image for similarity comparison. The output is a high-dimensional vector.
   
   3. 🔍 Vector Search with Qdrant  
      Using the image embedding, the vector is sent as a query to Qdrant — a high-performance vector similarity search engine — which returns the k most similar vectors from a pre-populated collection of labeled images.
   
   4. 💬 Class Prediction via Majority Voting  
      The payloads (metadata) of these returned neighbors include their labels. A majority vote is performed among the classes of the nearest neighbors to determine the predicted class for the input image.
   
   5. 🔁 Resolving Ties Dynamically  
      In cases where the top two classes have the same number of votes (e.g., 5 “forest” and 5 “beach”), a loop is triggered to increase the number of nearest neighbors (k) by 5 and re-perform the classification. This continues recursively until:
      - a clear winner is found, or
      - a maximum of 100 neighbors is reached (after which the top class is used, regardless of tie status)
   
   6. ✅ Return Final Class  
      Once the final decision is made, the identified class label is returned to the user.
   
   —
   
   🧪 Results & Performance
   
   The workflow was tested on a dedicated test split of the dataset — one that was never uploaded to Qdrant, ensuring it had not “seen” these examples before. With just basic model usage, no fine-tuning, and no metric learning involved, the classifier achieved an accuracy of:
   
   📈 93.24% on the test dataset
   
   This high accuracy demonstrates how far you can go with smart pre-labeling, well-chosen models for embedding, and a transparent majority voting mechanism embedded in a loop that gracefully resolves class ambiguities.
   
   —
   
   🌐 Third-Party API Integrations
   
   The seamless classification pipeline is powered by two intelligent API services:
   
   1. Voyage AI  
      - Role: Embedding image input into multimodal high-dimensional vectors.
      - Endpoint: https://api.voyageai.com/v1/multimodalembeddings
      - Model Used: voyage-multimodal-3
   
   2. Qdrant Cloud  
      - Role: Storing the labeled vector collection and performing similarity search.
      - Endpoint: Custom Qdrant Cloud endpoint (e.g., https://{cluster-id}.cloud.qdrant.io)
      - Collection: "land-use" (need to pre-upload your dataset)
   
   Both APIs require authentication, but usage of the free tiers is typically sufficient for experimentation or education purposes.
   
   —
   
   ⚙️ Reproducing the Workflow
   
   To adapt or rebuild this classifier, follow these steps:
   
   1. Download the lands dataset from Kaggle  
   2. Preprocess and embed each image using the same Voyage model  
   3. Upload embeddings (with labels) to Qdrant Cloud under a collection  
   4. Import the n8n workflow into your instance  
   5. Connect your corresponding credentials (Voyage API key, Qdrant API)  
   6. Run the workflow by passing an image URL  
   
   Optional: You can enhance it with confidence scores, returning the top N predictions, or integrating it into a web frontend for visual feedback.
   
   —
   
   🧩 Why This Matters
   
   This pipeline is a great example of machine learning democratization:
   
   - No need to create or train a deep learning model from scratch.
   - Fully explainable — every prediction is based on actual neighbors, not a black box.
   - Easily customizable — swap the collection and retrain it on cars, pets, or medical X-rays.
   - Leverages modern, serverless infrastructure without managing ML models locally.
   
   In short, this template empowers developers, data scientists, and even non-technical users to deploy machine learning workflows that are accurate, scalable, and interpretable — all in under an hour.
   
   —
   
   🚀 Conclusion
   
   From a tool for satellite image classification to a generalized KNN classifier for any dataset, n8n combined with Voyage AI and Qdrant proves to be a nimble stack for building near-production-ready AI workflows. The no-code/low-code nature makes it accessible to a broad audience, proving that advanced machine learning is no longer just the domain of data scientists.
   
   Try the workflow, experiment with new datasets, and bring AI-powered classification to your next automation project.
   
   —
   
   ✨ Bonus: Open Dataset Used  
   Land Use Scene Classification: https://www.kaggle.com/datasets/apollo2506/landuse-scene-classification
   
   —
   
   Tags: #n8n #VoyageAI #Qdrant #ImageClassification #SatelliteImagery #ComputerVision #NoCodeAI #KNNClassifier #Automation #LandUseDetection

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.