Dynamic AI Model Router for Query Optimization with OpenRouter

The Agent Decisioner is a dynamic, AI-powered routing system that automatically selects the most appropriate large language model (LLM) to respond to a user's query based on the query’s content and purpose.

This workflow ensures dynamic, optimized AI responses by intelligently routing queries to the best-suited model.

Advantages

🔁 Automatic Model Routing:** Automatically selects the best model for the job, improving efficiency and relevance of responses.

🎯 Optimized Use of Resources:** Avoids overuse of expensive models like GPT-4 by routing simpler queries to lightweight models.

📚 Model-Aware Reasoning:** Uses detailed metadata about model capabilities (e.g., reasoning, coding, web search) for intelligent selection.

📥 Modular and Extendable:** Easy to integrate with other tools or expand by adding more models or custom decision logic.

👨‍💻 Ideal for RAG and Multi-Agent Systems:** Can serve as the brain behind more complex agent frameworks or Retrieval-Augmented Generation pipelines.

How It Works

Chat Trigger: The workflow starts when a user sends a message, triggering the Routing Agent.
Model Selection: The AI Agent analyzes the query and selects the best-suited model from the available options (e.g., Claude 3.7 Sonnet for coding, Perplexity/Sonar for web searches, GPT-4o Mini for reasoning).
Structured Output: The agent returns a JSON response with the user’s prompt and the chosen model.
Execution: The selected model processes the query and generates a response, ensuring optimal performance for the task.

Set Up Steps

Configure Nodes:
Chat Trigger: Set up the webhook to receive user messages.
Routing Agent (AI Agent): Define the system message with model strengths and JSON output rules.
OpenRouter Chat Model: Connect to OpenRouter for model access.
Structured Output Parser: Ensure it validates the JSON response format (prompt + model).
Execution Agent (AI Agent1): Configure it to forward the prompt to the selected model.

Connect Nodes:
Link the Chat Trigger to the Routing Agent.
Connect the OpenRouter Chat Model and Output Parser to the Routing Agent.
Route the parsed JSON to the Execution Agent, which uses the chosen model via OpenRouter Chat Model1.

Credentials:
Ensure OpenRouter API credentials are correctly set for both chat model nodes.

Test & Deploy:
Activate the workflow and test with sample queries to verify model selection logic.
Adjust the routing rules if needed for better accuracy.

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