We present the artifacts that accompany our ASE 2024 paper, entitled \textit{Demonstration-Free: Towards More Practical Log Parsing with Large Language Models}. The artifacts include the LogBatcher tool, experimental data, and scripts. The source code of LogBatcher is available on Github. The artifacts include a step-by-step guide to reproduce the study. Full replication of the study requires 8GB of memory, 4 CPU cores, 20GB of disk space, and an OpenAI API key. To facilitate the reproduction of our study, we also provide a pre-installed Docker image. We believe that the artifacts are both \textit{available} and \textit{reusable}.