AgentLogs：开源 GitHub 云端智能体大规模执行日志数据集

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Abstract:Coding agents now complete development tasks in real software projects and submit the results as pull requests. A pull request shows the outcome of an agent's execution but not the process behind it, such as which files the agent read, which commands it ran, or how many tokens it consumed. This is recorded only in the agent's log, and no existing dataset provides such logs of real-world use, including failed agent executions, across a wide range of repositories. We introduce AgentLogs, a large-scale dataset of activity on GitHub's cloud agent platform on public repositories, collected without requiring opt-in from developers. AgentLogs comprises 363,624 tasks and 635,886 sessions, each session an execution of the agent, in 40,565 popular public repositories, with 75,631,136 log entries that record each session step by step, including prompts, intermediate reasoning, tool calls (e.g., file edits, git operations, and GitHub interactions), and token usage, together with the linked pull requests, timelines, workflow runs, and users. Sessions are included regardless of their outcome. AgentLogs enables research on agent behavior and failure modes, efficiency and cost, task formulation, human-agent collaboration, and repository adoption. A demonstration video can be found at: this https URL

Subjects:	Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2608.29204 [cs.SE]
	(or arXiv:2608.29204v2 [cs.SE] for this version)
	https://doi.org/10.48550/arXiv.2608.29204 arXiv-issued DOI via DataCite

Submission history

From: Jonan Richards [view email]
[v1] Sat, 29 Aug 2026 11:38:19 UTC (551 KB)
[v2] Sat, 24 Oct 2026 03:20:53 UTC (709 KB)