router-rule-tuner

To optimize AI routing configurations, you must analyze router decision logs to identify borderline scores and adjust thresholds, weights, and keywords. This process shifts simple queries to local models, reducing paid API token usage without sacrificing output quality.

Log analysis identifies inefficient model selection by examining routing patterns and borderline decision scores within OpenClaw Router logs. It reveals when simple queries are unnecessarily routed to expensive API models instead of local models.

To tune router thresholds and shift requests to local models, you need access to historical OpenClaw Router decision logs and the current OpenClaw configuration file. These inputs allow the system to map usage patterns and suggest precise weight adjustments.

Configuration tuning can favor local models without degrading quality by analyzing borderline decision scores in router logs. It intelligently shifts only appropriate simple tasks to local models by adjusting specific keywords and thresholds based on actual usage patterns.

The best way to analyze OpenClaw Router decision logs for cost-efficiency is to process usage patterns and borderline decision scores directly. This approach pinpoints exact threshold and weight modifications needed to keep simple requests on local models.

Your router sends simple queries to expensive API models because current thresholds and weights are misconfigured for your usage patterns. Analyzing borderline decision scores in logs identifies the exact configuration adjustments needed to reroute them locally.