I measured my multi-agent idea. It failed. I shipped the autopsy instead (github.com)

A new tool called B.I.O.M.A. has been introduced, designed to enhance the efficiency and security of large language model (LLM) applications. This drop-in micro-kernel, written in Rust and paired with a lightweight Python layer, optimizes the processing of prompts by significantly reducing energy costs and token usage. Key features include "context apoptosis," which minimizes wasted input tokens by up to 97% in lengthy sessions, and a "cognitive firewall" for secret redaction and flood detection, all while functioning locally and remaining provider-agnostic. The significance of B.I.O.M.A. lies in its ability to streamline interactions with multiple LLM providers such as Anthropic, Google, and OpenAI without sacrificing answer quality. Benchmarked results indicate a drastic reduction in input token usage, leading to energy savings of over 97% while maintaining accuracy. Developers can implement this tool easily by embedding it within existing architectures, thereby enhancing both performance and security without requiring extensive modifications to their codebases.