This article provides a complete, practical walkthrough of the Splitout Summarize Automation 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:  
   Effortlessly Compare LLMs with n8n, OpenAI, and Google Sheets
   
   Meta Description:  
   A powerful n8n workflow for comparing large language models (LLMs) like OpenAI’s GPT-4 and Mistral. Evaluate their responses side-by-side in a chat UI and log results to Google Sheets for easy review and analysis.
   
   Keywords:  
   n8n workflow, LLM comparison, OpenAI, Mistral, Vertex AI, Google Sheets, OpenRouter, AI evaluation, GPT-4, langchain integration, chatbot testing, model selection, AI benchmarking, prompt engineering, large language models
   
   Third-Party APIs and Services Used:
   
   1. OpenRouter API (used for multi-model LLM interaction across providers)  
   2. OpenAI (via OpenRouter or directly to compare model variants such as gpt-4.1)  
   3. Google Sheets API (for structured logging and analysis of model responses)  
   4. n8n LangChain integrations  
   5. Optional: Vertex AI (if enabled and connected manually)
   
   Article:
   
   Easily Compare LLMs Using OpenAI and Google Sheets with This n8n Workflow
   
   In the ever-expanding landscape of large language models (LLMs), deciding on the best model for your use case can be notoriously difficult. With LLMs varying in cost, performance, output tone, memory capabilities, and more—testing and evaluating multiple models through real-world prompts is the key to building scalable and reliable AI applications.
   
   That’s why we created an automated n8n workflow called “Easily Compare LLMs Using OpenAI and Google Sheets”—designed to help developers and teams evaluate language models objectively and transparently. All without writing custom scripts or juggling Python notebooks.
   
   Read on to explore how this workflow allows you to test multiple models—like GPT-4.1 and Mistral-large—in real-time, side-by-side, while storing the results in a collaborative Google Sheet for manual or automated assessment.
   
   🔧 Why You Need This
   
   When you're developing a chatbot, data labeling interface, content generator, or any AI-powered service, choosing the right LLM can define the product’s success. But LLMs (Large Language Models) are inherently non-deterministic and can behave differently depending on prompt, memory context, and even model updates.
   
   This n8n workflow solves that by allowing you to:
   
   - Run the same user input through multiple models simultaneously  
   - View their responses side-by-side within a chat UI  
   - Automatically record each model’s response, prior memory context, and session metadata to Google Sheets  
   - Collaborate with your team or evaluate model answers programmatically using another LLM  
   
   🧠 How It Works
   
   Here’s a high-level overview of what happens inside this workflow:
   
   1. A user sends a message via the chat interface.
   2. The input is duplicated and fed into two LLMs (e.g., GPT-4.1 and Mistral-large).
   3. Each model processes the same input using its own isolated memory context.
   4. Both responses, along with the prompt, model ID, and chat history, are logged into a Google Sheet.
   5. In the frontend chat UI, both responses are concatenated and returned to the user for immediate comparison.
   6. You can manually score the responses (e.g., “Good”, “Correct”, “Bad”) or later automate evaluation with models like OpenAI’s GPT-4.
   
   📊 The Google Sheets Advantage
   
   Instead of analyzing model results in a Jupyter Notebook or a JSON blob, results are sent straight to a spreadsheet through Google Sheets’ API.
   
   You get structured data with columns like:
   
   - sessionId  
   - model_1_id, model_2_id  
   - user_input  
   - model_1_answer, model_2_answer  
   - context_model_1, context_model_2  
   - model_1_eval, model_2_eval (optional, by human or automated rating)
   
   This makes it easy to sort, filter, and rank results—even for non-technical stakeholders or product managers contributing to model evaluation.
   
   💡 Real-World Use Cases
   
   This workflow is perfect for:
   
   - Evaluating the performance of different LLM providers (e.g., OpenAI vs. Mistral via OpenRouter)  
   - Comparing model tiers (e.g., GPT-4 vs. GPT-3.5, or GPT-4.1 vs. GPT-4-Turbo)  
   - Testing how well models handle specific tools, prompts, or system instructions  
   - Building datasets for prompt tuning or fine-tuning  
   - A/B testing before deployment into production workflows  
   
   ⚙️ Customization Made Simple
   
   Setup is straightforward and tweakable:
   
   - Copy this Google Sheet template: Template - Easy LLMs Eval  
   - Customize your comparison models in the "Define Models to Compare" node (e.g., openai/gpt-4.1 vs mistralai/mistral-large)  
   - Modify the agent’s system prompt and tools to match your domain (e.g., medical, legal, customer support)  
   - Want to compare 3 or more models? Extend the loop and adjust the spreadsheet schema  
   
   🧩 Modular Nodes with Langchain & OpenRouter
   
   This workflow uses powerful modular nodes from n8n’s LangChain integration such as:
   
   - AI Agent (for invoking the LLM)  
   - Memory Buffer and Chat Memory Manager (to isolate memory per model)  
   - OpenRouter Chat Model (to dynamically route to LLMs from multiple providers on-demand)  
   - Google Sheets (via service account credentials)
   
   OpenRouter enables cross-provider evaluation using a simple model ID format like "openai/gpt-4.1" or "mistralai/mistral-large", without having to change node configurations each time.
   
   🧠 About Context & Memory
   
   Each model’s chat history is isolated using the sessionId + model name. This ensures fair comparison as both models receive the same input under consistent memory conditions.
   
   Prior user-model exchanges are stored with each session and logged to Google Sheets—critical for analyzing how context affects responses over time.
   
   💸 Token & Cost Awareness
   
   Because each prompt is sent to two LLMs, token usage and cost effectively double. Keep an eye on token limits—especially with long prompts or frequent testing.
   
   For organizations with budget sensitivities, you can:
   
   - Use cheaper model versions (e.g., gpt-3.5 instead of gpt-4)  
   - Optimize prompts for brevity  
   - Sample fewer inputs initially  
   
   🧪 Bonus: Automate Evaluation
   
   Want to offload human review? You can create a subworkflow that uses a higher-quality model (like GPT-4 or o3 from OpenAI) to rate outputs automatically by assessing coherence, accuracy, tone, or domain knowledge.
   
   This is advanced and should be used with caution—but is powerful for scaling model evaluations when human reviewers are constrained.
   
   🚀 Get Started Today
   
   - Duplicate this Google Sheets template  
   - Import the n8n workflow  
   - Customize the models and prompts  
   - Run test inputs and review results  
   - Iterate your system prompts based on insights
   
   Choosing the right LLM may take iteration—but now, with this workflow, you can be confident your decisions are based on structured evaluations rather than guesswork.
   
   Whether you’re a solopreneur exploring AI, or an enterprise building product-ready agents, you now have a no-code ally in LLM testing at scale.
   
   Start comparing. Start optimizing. Start deploying smarter AI.

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.