SWE-chat: Coding Agent Interactions From Real Users in the Wild

arXiv:2604.20779v2 Announce Type: replace Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We present SWE-chat, the first large-scale dataset of real coding agent sessions collected from open-source developers in the wild. The dataset currently contains almost 18,000 sessions, comprising more than 229,000 user prompts and 2 million agent tool calls. SWE-chat is a living dataset; our collection pipeline automatically and continually discovers and processes sessions from publi

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