AgentLogs：用于揭开GitHub云智能体黑箱的数据集
AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agent
- Radboud University（拉德堡德大学）
- Nara Institute of Science and Technology（奈良科学技术研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究推出AgentLogs数据集，包含GitHub上35810个热门仓库的智能体任务、会话及6400余万条日志，可用于研究智能体行为、协作等，填补了智能体贡献过程数据的空白。
AI中文摘要：
基于生成式AI的软件工程智能体正成为实际软件项目的常规贡献者。在GitHub上，开发者可向Copilot云智能体分配任务，该智能体会自主探索代码仓库、编辑代码、运行命令、打开或审查拉取请求，并生成每一步的详细日志。现有数据集虽能捕捉智能体贡献的结果，如智能体生成的拉取请求，但智能体产生这些贡献的过程仍基本未被探索。为填补这一空白，我们推出AgentLogs，这是GitHub上智能体活动的大规模数据集。AgentLogs包含我们扫描的1812362个热门公共仓库中的35810个仓库里的307416个智能体任务和549239个智能体会话，以及64255174条会话日志条目，这些条目逐步骤记录每次智能体运行，包括提示、中间推理、工具调用（如文件编辑、git操作和GitHub交互）以及token使用情况。AgentLogs不仅揭示智能体贡献了什么，还揭示其运作方式，从而支持对智能体软件工程中的智能体行为、效率与成本、任务制定、失败模式以及人机协作的研究。
英文摘要：
Generative AI-based software engineering agents are becoming routine contributors to real-world software projects. On GitHub, developers can assign tasks to the Copilot cloud agent, which autonomously explores the repository, edits code, runs commands, and opens or reviews pull requests, producing a detailed log of every step along the way. While existing datasets capture outcomes of agent contributions, such as agent-authored pull requests, the process by which agents produce these contributions remains largely unexplored. To address this gap, we introduce AgentLogs, a large-scale dataset of agent activity on GitHub. AgentLogs comprises 307,416 agent tasks and 549,239 agent sessions in 35,810 of the 1,812,362 popular public repositories that we scanned, together with 64,255,174 session log entries that record each agent run step by step, including prompts, intermediate reasoning, tool calls (e.g., file edits, git operations, and GitHub interactions), and token usage. By exposing not only what agents contribute but also how they work, AgentLogs enables research on agent behavior, efficiency and cost, task formulation, failure modes, and human-agent collaboration in agentic software engineering.
