Show HN: KORA Doctor – find LLM calls your agent may not have needed (github.com)

KORA Doctor has been introduced as a lightweight, open-source command-line interface (CLI) tool designed to optimize AI agent performance by analyzing the AUDR usage data and identifying potentially unnecessary model calls. This tool is significant for the AI/ML community as it addresses the growing need for efficiency in deploying large language model (LLM) agents, potentially reducing costs and enhancing performance by suggesting which calls can be cached, made deterministic, replaced with cheaper models, or eliminated altogether. KORA Doctor assesses various aspects of model usage—such as duplicate calls, caching opportunities, and orchestration overhead—displaying its findings in machine-readable formats. For instance, in its early tests, it flagged over 89% of costs from an example agent run as being avoidable. With a simple installation from GitHub and a focus on heuristic analysis, KORA Doctor aims to support developers in pinpointing key inefficiencies without altering their agents or introducing complexity, promoting a more streamlined approach to optimizing AI interactions. By gathering feedback from real usage data, it hopes to evolve into a more robust optimization tool in the future.