Feature: Intelligent Model Routing for Cost & Performance Optimization

Overview

To optimize both response speed and token expenditure, we need to implement a Model Routing system. Currently, sending every request to a large, expensive model (e.g., GPT-4o or Claude 3.5 Sonnet) is inefficient for simple tasks. This feature will allow PicoClaw to intelligently choose between "Small/Efficiency" models and "Large/Reasoning" models based on the complexity of the user's request.

Core Requirements

• Tiered Model Configuration: Allow users to define at least two tiers of models in their configuration (e.g., efficiency_model for simple tasks and power_model for complex ones).

• Task Classification: Implement a lightweight triage mechanism (using regex, a very small local model, or a fast LLM call) to categorize the intent.

• Easy Tasks: Simple greetings, time checks, basic formatting, or single-step commands.

• Hard Tasks: Multi-step reasoning, complex coding, or tasks requiring deep creative writing.

• Routing Logic: A dispatcher that sends the payload to the appropriate model based on the classification result.

• Fallback Mechanism: If the "Small" model fails to provide a valid or confident response, the system should automatically escalate the task to the "Large" model.

Expected Impact

• Cost Reduction: Significant decrease in token fees for daily, repetitive interactions.
• Latency Improvement: Faster response times for simple queries using optimized "Mini" models.