Hcompany/h_cua_perf

H CUA Perf: computer-use agent traces for serving benchmarks

A dataset of agent traces collected by H Company while evaluating its computer-use agent on CUA-Gym desktop tasks, packaged for replay against an LLM inference server with AIPerf (--custom-dataset-type mooncake_trace).

Every record is one real chat-completion request the agent made, with its recorded think time. Replaying the dataset reproduces the request pattern of a tool-calling, screenshot-driven agent: prompts that grow with every step, a bounded number of images per request, tool definitions on every call, and a prefix that is shared with the previous request only up to the point where the agent's context cleaning replaced a screenshot.

What the dataset is

• 477 trajectories, 18,224 requests. 38.2 steps per trajectory on average (median 23), from 1 to 201, the harness capping trajectories at 200 steps.
• Balanced outcomes and lengths. 239 trajectories succeeded at their task and 238 failed. Each trajectory is a different task. Trajectories were sampled in five step-count buckets, so short and long episodes are about equally represented.
• Mooncake trace format. One OpenAI chat-completion request per agent step, with the inter-request delay (the agent's think time between the previous response and the next request, in milliseconds) and output_length (the recorded completion tokens). There is no absolute timestamp: the trajectories were recorded independently of each other, so the dataset replays at a chosen concurrency or request rate, each session's turns spaced by their recorded delays. session_id is the task id; a trajectory's records are contiguous.
• Tool calling. The agent drives the desktop through a shell and file tools rather than GUI actions. Every request carries the 10 tool definitions in the record's tools field (update_plan, shell, poll_execution, send_execution_input, terminate_execution, read_file, write_file, search_replace, document_ocr, answer) and tool_choice: auto in extra. Assistant messages hold tool_calls; observations come back as tool messages.
• Screenshots. The task screenshot arrives with the first user message; later ones are returned inside tool results. This source build keeps one screenshot per request, the latest, and replaces every earlier one in place by the text placeholder [Image omitted by context cleaning], exactly as the agent's own context cleaning did at collection time. Screenshots are PNG data URLs; each image part also carries a uuid (xxh3-64 of the data URL) so a replay can send uuid-only references to a server that already holds the image.
• Prompt growth. The prompt holds the system prompt and the full text history of the trajectory (tool calls and tool results included), so prompt length and KV-cache footprint grow roughly linearly with the step index while the number of images stays bounded. The average record is 0.5 MB and the largest 2.6 MB.

Files

File	Content
h_cua.jsonl.zst	The source dataset, zstd-compressed (2.5 GB; 9.3 GB decompressed), one screenshot per request
h_cua.meta.json	Manifest: record and trajectory counts, and the number of requests of every trajectory
trace_processor.py	Derives replay variants from the source: screenshot window, trajectory sample, trajectory lengths

Record format

{
  "session_id": "0077a580-d847-507b-a7e1-8cf2b024a23e",
  "delay": 2100,
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}, "uuid": "..."}, {"type": "text", "text": "..."}]},
    {"role": "assistant", "content": "...", "tool_calls": [{"id": "...", "type": "function", "function": {"name": "shell", "arguments": "{...}"}}]},
    {"role": "tool", "tool_call_id": "...", "content": [{"type": "text", "text": "...\n\n[Image omitted by context cleaning]\n\n..."}]}
  ],
  "tools": [{"type": "function", "function": {"name": "shell", "description": "...", "parameters": {...}}}],
  "output_length": 187,
  "extra": {"tool_choice": "auto"}
}

The first request of a trajectory has no delay. A few requests (25 of 18,224) were made without tools and carry neither tools nor extra; they are kept as recorded.

Replaying

How to replay a mooncake_trace dataset with AIPerf, at a chosen concurrency or request rate with each session's turns spaced by their recorded delays, and how to derive variants (a wider screenshot window, fewer or shorter trajectories) is covered in the AIPerf documentation (link to follow).

How the traces were collected

The tasks are the desktop training tasks of CUA-Gym, run in CUA-Gym's desktop environment. The agent is H Company's computer-use agent driven over a shell and file tools, with a Qwen3.8-27B policy (qwen-3-8-27b), evaluated by CUA-Gym's own task verifiers, which is where the success and failure labels come from. Traces were recorded on 2026-09-08 and 2026-09-09. From that collection, the trajectories were sampled as described above; nothing inside a request was altered except the screenshot windowing.

Acknowledgements

The tasks and environments come from CUA-Gym. Task data from CUA-Gym (xlang-ai) is licensed CC BY 4.0. If you use this dataset, please also cite:

@misc{wang2026cuagymscalingverifiabletraining,
      title={CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents},
      author={Bowen Wang and Dunjie Lu and Junli Wang and Tianyi Bai and Shixuan Liu and Zhipeng Zhang and Haiquan Wang and Hao Hu and Tianbao Xie and Shuai Bai and Dayiheng Liu and Que Shen and Junyang Lin and Tao Yu},
      year={2026},
      eprint={2605.25624},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2605.25624},
}

License

This dataset is released under CC BY 4.0. The task instructions and screenshots derive from CUA-Gym task data, itself CC BY 4.0; keep the attribution above when redistributing.