README.md

metadata

pretty_name: Cloud-OpsBench
language:
  - en
license: mit
tags:
  - cloud-ops
  - root-cause-analysis
  - kubernetes
  - agentic-evaluation
  - microservices
size_categories:
  - n<1K
configs:
  - config_name: default
    default: true
    data_files:
      - split: benchmark
        path: index.jsonl

Cloud-OpsBench — Official Dataset Distribution

This is the official dataset distribution of Cloud-OpsBench, maintained by LLM4Ops. The canonical code and benchmark implementation repository is LLM4Ops/Cloud-OpsBench on GitHub. This Hugging Face repository distributes the benchmark artifacts, not the agent or evaluation implementation.

Cloud-OpsBench evaluates agentic root cause analysis in Kubernetes-based cloud systems. Each incident is a preserved state snapshot, allowing diagnostic tools to replay recorded evidence without a running cluster. A correct joint diagnosis identifies both the faulty component and its root cause.

Release and provenance

• Distribution version: v1.0.0.
• Source GitHub commit: ea05dafbc87b5985df8721622d63d4229c2b4224.
• Cases: 754 — Online Boutique 550, TrainTicket 204.
• release.json records source identity, per-system/category counts, and data-tree IDs.
• manifest.json records each distributed file's size and SHA256, excluding the manifest itself.

The three artifact directories are exported byte-for-byte from the specified GitHub commit. The card, case index, and release manifests are distribution metadata. Version numbers identify dataset distributions, not new experimental splits or benchmark scores. Other redistributed or transformed copies should identify this source and the version/commit they derive from.

Contents

benchmark/<system>/<category>/<case_id>/
    metadata.json
    tool_cache.json
    raw_data/
    code/                     # Online Boutique diagnostic source files
golden-trajectory/<system>/<category>/<case_id>/
    path1.json
    path2.json
process-label/<system>/<category>/<case_id>/
    milestone.json

• Benchmark snapshots: incident query, namespace, difficulty, ground-truth component/root cause, cached diagnostic-tool responses, Kubernetes states, logs, alerts, available metrics, and application source where present.
• Process labels: milestone annotations, admissible evidence, and dependency edges for process evaluation.
• Golden trajectories: two auxiliary expert diagnostic trajectories per incident. They are not the input to the current milestone-based process scoring.

index.jsonl has one row per incident, including case_key, system, category, case_id, namespace, query, difficulty, fault_taxonomy, fault_object, root_cause, and relative paths to the metadata, tool cache, milestone, and two golden trajectories. The Dataset Viewer displays this index only, not a merged interpretation of all the heterogeneous JSON artifacts.

Download and use

Install huggingface_hub, then download the complete snapshot:

from huggingface_hub import snapshot_download

dataset_dir = snapshot_download(
    repo_id="LLM4Ops/Cloud-OpsBench",
    repo_type="dataset",
    revision="main",  # For reproducible experiments, pin an HF commit or release tag.
    local_dir="cloudopsbench-data",
)

Use the tools, agents, MCP interface, and evaluator from the GitHub implementation. After cloning that repository, the three downloaded artifact directories can be placed at its root, preserving their original relative paths. Match the dataset's source commit to the implementation when reproducing a release.

For a lightweight index without downloading every snapshot:

from datasets import load_dataset

cases = load_dataset("LLM4Ops/Cloud-OpsBench", split="benchmark")

Loading the index does not download the incident files referenced by each row; use snapshot_download for the replay artifacts. All released cases are exposed as a single benchmark split. No official training/validation/test division is introduced by this distribution.

Evaluation boundary and limitations

Task agents should receive the incident query/namespace and acquire observations through diagnostic tools. Ground-truth result fields, milestone labels, and golden trajectories are evaluator/research resources; do not expose them to an agent during benchmark evaluation. The distribution is public, but a runner must enforce this separation.

These are recorded incidents from two microservice systems, not a representative sample of every production environment. Snapshot replay does not test live remediation or changing cluster state. Use the GitHub implementation for the diagnostic tool semantics and evaluation protocol.

License

The project's MIT license is reproduced in LICENSE. Original notices in embedded third-party source files are retained; distribution does not replace their original licenses or claim authorship of that source.

Citation

@article{wang2026cloud,
  title={Cloud-OpsBench: A reproducible benchmark for agentic root cause analysis in cloud systems},
  author={Wang, Yilun and Yu, Guangba and Huang, Haiyu and Huang, Yujie and Wang, Zirui and Chen, Pengfei and Lyu, Michael R},
  journal={arXiv preprint arXiv:2603.00468},
  year={2026}
}
@inproceedings{yu2026freezing,
  title={Freezing the Crime Scene: A State Snapshot Paradigm for Reproducible Agentic SRE Evaluation},
  author={Yu, Guangba and Wang, Yilun and Lyu, Michael R},
  booktitle={Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering},
  pages={1733--1737},
  year={2026}
}

For reproducibility, also report the HF revision or distribution version and source commit from release.json. Report issues and propose case changes in the canonical GitHub repository.