KostAI

The project provides an open‑source, local‑first toolkit that inserts a control layer into any LLM‑powered workflow. It measures each AI call, routes the request to an appropriately sized model, applies compression or caching, and enforces quality gates before the token cost is incurred. The system is intended to be installed quickly without wizards or configuration files, allowing teams to run a pilot on a small workload and see a plain‑English report of savings.

It targets developers, operations teams, and product managers who need to manage token spend for AI agents, research assistants, or content‑generation pipelines. By offering voluntary “skills” that can be adopted per‑user or per‑team, it avoids mandatory telemetry or centralized monitoring, while still delivering auditability for managers.

What distinguishes the toolkit is its emphasis on measured pilots and proof‑before‑rollout, using a routing architecture that can reduce token costs by up to ninety‑two percent in demonstrated tests. The code is MIT‑licensed, hosted publicly on GitHub, and designed to integrate with existing LLM stacks without requiring a separate dashboard.