Artifact for "LGTM! Characteristics of Auto-Merged LLM-based Agentic PRs"

AI tools are generating code faster than humans can properly review it, leading repositories to skip review and auto-merge agentic Pull Requests (PR) directly. In our study, we analyze the characteristics of auto-merged agentic PRs and compare them to human-authored ones. We examine code characteristics, repository ecosystems, and agentic tools across the AIDev dataset, spanning diverse software engineering tasks. In this artifact, we provide the source-code, mined data, and scripts to analyze the data. We find that auto-merged PRs are smaller and more focused, and that repositories tend to either auto-merge all or none agentic PRs, with more mature repositories favoring the latter. Compared to human-authored auto-merges, maintainers auto-merge agentic PRs more often but show caution toward PRs that delete existing code. Among agents, OpenAI Codex and Claude Code receive the highest auto-merge rates. These findings can inform agentic tool design and repository's auto-merge decisions.