This article provides a complete, practical walkthrough of the Splitout Googledocs 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:
   🦙🔍 Comparing Ollama Vision Models: An Automated Image Analysis Workflow in n8n
   
   Meta Description:
   Discover how to automate detailed image analysis with multiple locally-hosted Ollama Vision models using n8n. Learn how this workflow extracts insights, compares model outputs, and saves results to Google Docs.
   
   Keywords:
   Ollama Vision, vision models, image analysis automation, n8n workflow, AI image processing, Ollama LLM, Google Drive API, Google Docs API, base64 image processing, markdown formatting, local LLMs, image recognition, structured image data, multi-model comparison
   
   Third-Party APIs Used:
   1. Google Drive API (for downloading image files)
   2. Google Docs API (for saving model outputs to a document)
   3. Ollama (Local API – http://127.0.0.1:11434/api/chat – for processing vision model requests)
   
   Article:
   
   🦙👁️👁️ Automate and Compare Local Vision Models with n8n: A Comprehensive Image Analysis Workflow
   
   In a world increasingly driven by visual content, the ability to understand and analyze images with precision is paramount. Whether for real estate marketing, product validation, academic research, or creative projects, automated image insights can be a game-changer. This article explores a robust, no-code n8n workflow that leverages local Ollama vision models for detailed image analysis—then compares their outputs and saves findings to Google Docs for easy collaboration and further exploration.
   
   What Is Ollama?
   
   Ollama is an open-source platform that allows users to run powerful large language models (LLMs) locally, including models with vision capabilities. This empowers users to retain full control over data and privacy, run inference on their own hardware, and avoid API costs. Ollama supports various models designed for different tasks—from comprehension to code generation—and now, with models like Granite3.2-Vision and Llama3.2-Vision, you can apply them to image understanding tasks.
   
   What Does This Workflow Do?
   
   This unique n8n automation enables users to evaluate the effectiveness of multiple vision models on the same image file. The process includes:
   
   1. Downloading an image from Google Drive.
   2. Converting that image into a base64-encoded payload suitable for LLMs with vision capabilities.
   3. Looping through a provided array of local Ollama Vision models (e.g., granite3.2-vision, llama3.2-vision, gemma3:27b).
   4. Sending the model-specific image prompts to each vision model via HTTP POST requests.
   5. Collecting the textual outputs from each model, formatted in structured markdown with headers.
   6. Saving all model responses side-by-side in a Google Docs document for human review and comparison.
   
   The beauty of this approach lies not only in the automation but also in its scalability and flexibility. You can easily update the image, prompt, or models being used, making it immensely useful for benchmarking or building internal AI tools.
   
   Use Cases
   
   The workflow serves a variety of professional contexts, including:
   
   - Marketing Teams: Automatically analyze product photos for brand visibility and packaging integrity.
   - Real Estate Professionals: Extract critical metrics or context from listing flyers or data charts.
   - Researchers: Test LMMs' ability to recognize differences in datasets or interpret experimental images.
   - Data Analysts: Compare different model outputs for the same image and evaluate model performance.
   
   How It Works: Node-by-Node Breakdown
   
   This n8n workflow is cleverly structured and modular. Here’s a summarized insight into its core nodes and logic:
   
   1. Manual Trigger: The user kicks off the process by testing the workflow from the n8n interface.
   
   2. Google Drive Integration: Once triggered, the workflow downloads a specified image stored on Google Drive using the Google Drive node.
   
   3. Convert Image to Base64: Using the “Extract from File” node, the image is converted into a base64 string, which is required by LLMs for visual reasoning.
   
   4. Model Listing: The workflow prompts a Set node to define the array of vision models to test (granite3.2-vision, llama3.2-vision, gemma3:27b).
   
   5. Looping Through Models: The Split Out and Split In Batches nodes sequentially loop through the image for each listed vision model.
   
   6. Custom Prompt Injection: The “General Image Prompt” node injects a long-form prompt designed to guide the LLMs into performing detailed, structured vision analysis. The analysis covers:
      - Comprehensive object inventory
      - Contextual placement and background
      - Spatial relationships
      - Text and label extraction
      - All formatted in organized markdown output
   
   7. LLM HTTP Request: A POST request is sent to the locally running Ollama model with the payload comprising the image and customized prompt.
   
   8. Save Outputs: Results from each vision model are added under their respective headers inside a Google Docs document using the Google Docs API—making results easy to scan, compare, and discuss.
   
   Setup Instructions
   
   Using this workflow requires the following setup:
   
   - A running local instance of Ollama (https://ollama.com/)
   - Downloaded or pulled vision-compatible models for Ollama
   - Properly authenticated Google Drive and Docs integrations within your n8n environment
   - An image file stored on Google Drive (its ID needs to be plugged into the workflow)
   - Active internet access (if using Google APIs) and firewall permissions for local APIs
   
   Once configured, click "Test Workflow" and watch the automation unfold—from image extraction to model inference to documentation.
   
   Customization Suggestions
   
   Want to tailor the workflow to your use? Try the following:
   
   - Swap out Google Drive for Dropbox, S3, or another file provider.
   - Alter the “General Image Prompt” to extract brand data, identify threats, or summarize text overlays.
   - Add Slack or Notion integration for instant notifications or database recording.
   - Enable advanced logic to select the "best" model response based on token count, sentiment, or keyword density.
   
   Key Features Recap
   
   ✔️ Multi-Model Testing: Simultaneously tests several models on the same image.
   
   🧠 Detailed Prompting: Extracts visual information in exhaustive and structured markdown format.
   
   📄 Google Docs Integration: Automatically saves all model responses in a collaborative document.
   
   🔐 Local Model Runtime: Keeps data private, bypasses API rate limits, and improves speed with edge processing.
   
   Conclusion
   
   With this n8n-based automation, you can streamline how you interact with vision-capable large language models. By providing a plug-and-play testing environment, this workflow simplifies a traditionally complex task—analyzing and comparing image description models—into a repeatable, shareable, and extensible process. Whether you're a developer vetting vision models, or an analyst extracting structured visual data at scale, this workflow is your intelligent assistant for everything images can reveal.
   
   🦙👁️ Start automating, start comparing—and find your best local Ollama Vision model today.

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.