Logzip

Logzip addresses the challenge of analyzing large log files with Large Language Models (LLMs) by compressing them into a human- and LLM-readable format. It tackles context limit constraints and excessive noise by abstracting repetitive log patterns, achieving typical token savings of 40-60%. Engineered in Rust for performance and offering a convenient Python API, logzip is ideal for RAG pipelines and SRE analysis, allowing LLMs to process larger contexts more economically and accurately by highlighting critical anomalies over verbose, repetitive entries.

Key Features

Use Cases