Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DevOpsBench-100
DevOpsBench-100 is a synthetic long-horizon software-engineering / SRE agent benchmark: 100 tasks over one executable world ("NovaCart", a mid-size e-commerce SaaS) with 72 SQLite tables, 1451 seeded rows, a 38-file monorepo with 417 commits, and 97 MCP tools spanning a first-party engineering stack (tickets, PRs, CI, deployments, canaries, migrations, feature flags, metrics, alerts, incidents, chat, knowledge base) plus deliberately disagreeing vendor-shaped surfaces (Jira, Linear, GitHub Issues, Prometheus, Sentry, PagerDuty, Confluence, spreadsheets) and Kubernetes.
Tasks are outcome-only tickets (symptom + definition of done; company policy lives in the world's knowledge base, not the prompt). Reference trajectories run 4-34 tool calls (median 13). Acceptance is fully deterministic: each task ships an executable vcode verifier that checks the final world state and the append-only audit log, with anti-forgery table pins - no LLM judge, no network, no clock in the reward path.
What is included
data/tasks.jsonl: task records (task_id,task_name,world_id,prompt,context_files,rubric,gold_output,metadata).tasks/: one readable JSON record per task (includes the guided instruction variant).world/: the offline world source - stdlib MCP server, tool implementations, seeded SQLite database, schema and seed SQL.verifiers/: 100 standalone verifier scripts (python3 verify_<task>.py world.dbprints the full verdict).trajectories/: one executed reference trajectory per task (JSONL).reports/: measured build and qualification evidence.
Task families (19)
| Family | Tasks |
|---|---|
| Cross-system source of truth | 8 |
| Error-rate SLO recovery | 8 |
| Latency optimization | 8 |
| API migration | 7 |
| Change attribution | 7 |
| Feature-flag operation | 7 |
| Multi-service rollout | 7 |
| Security incident response | 7 |
| AIOps root-cause analysis | 6 |
| Flaky-test remediation | 6 |
| AIOps detection | 5 |
| AIOps localization | 5 |
| Cross-source reconciliation | 5 |
| Code implementation | 4 |
| Operational judgement / restraint | 4 |
| Incident handover | 2 |
| Long-horizon delivery | 2 |
| Human-approval gated change | 1 |
| Workspace scripting | 1 |
Objective release gates
| Gate | Required | Measured |
|---|---|---|
| Tasks | 100 | 100 |
| Oracle replays at reward 1.0 | 100/100 | see reports/qualification.json |
| Deterministic verifier replays | 100/100 | see reports/qualification.json |
| Negative-control false accepts | 0 | see reports/qualification.json |
| LLM / network calls in verifier | 0 | 0 |
Provenance and contamination
Every service, metric, document, commit, and incident is synthetic and was generated for the NovaCart world. 30 cross_system/handover tasks were ported from TheAgentCompany task shapes re-grounded onto NovaCart's own state; the AIOps families reproduce the microsoft/AIOpsLab task structure (detect / localize / analyze) against NovaCart. No third-party benchmark text, evidence, or gold answers are included. Gold outputs are public, so this release is appropriate for transparent evaluation and RL experiments rather than secret-test claims.
Licenses
Synthetic task data and world content are CC-BY-4.0. Benchmark code, server, and verifiers are Apache-2.0.
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