Hey everyone, ​I’ve been experimenting with token optimization for LLM agent frameworks by treating terminal and tool outputs as a data compression problem rather than a text-filtering one. ​The pipeline uses a bidirectional 42-stage architecture: ​Algorithmic Reduction: Raw text passes through an LTSC (LZ77-style lossless sequence compression) layer combined with LZW token substitution to eliminate repetitive terminal patterns dynamically. ​Structural Compaction: Code segments are reduced to AST skeletons, and nested JSON payloads are flattened into tabular structures (TOON) to minimize semantic token weights. ​0-Risk Fallback: A local comparison check runs at every stage. If a compression layer increases string length or corrupts format, it instantly rolls back. ​Response Filtering: A 7-stage outbound filter targets conversational boilerplate and normalizes whitespace.…