SWE-chat: Coding Agent Interactions From Real Users in the Wild
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 public repositories. Leveraging SWE-chat, we provide an initial empirical characterization of real-world coding agent usage and failure modes. We find tha...
| Authors | Joachim Baumann, Vishakh Padmakumar, Xiang Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 April 2026 |
| Last updated at source | 1 October 2026 |
Models and datasets referencing this paper
- SALT-NLP/SWE-chatDataset
- cfahlgren1/SWE-chatDataset
- agu18dec/chive-swechat-qwen3.6-27bDataset
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