Datasets:

experiential-labs
/

wmo-terminal-tasks-traces

Languages:

English

Tags:

agent-trajectories

world-models

llm-environments

License:

Dataset card Files Files and versions

xet

Community

Dataset Viewer

Duplicate

Split (1)

train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
key_mode: string
winner_spec: struct<label: string, reasoning: bool, knowledge: bool, verify: bool, grounder: string, workspace: b (... 37 chars omitted)
  child 0, label: string
  child 1, reasoning: bool
  child 2, knowledge: bool
  child 3, verify: bool
  child 4, grounder: string
  child 5, workspace: bool
  child 6, top_k: null
  child 7, demo_obs_cap: null
considered: list<item: string>
  child 0, item: string
val_traces: int64
winner_label: string
scores: struct<base: double, reason: double, reason+fetch: double, rag-deep: double, reason+kb: double, reas (... 18 chars omitted)
  child 0, base: double
  child 1, reason: double
  child 2, reason+fetch: double
  child 3, rag-deep: double
  child 4, reason+kb: double
  child 5, reason+verify: double
to
{'winner_label': Value('string'), 'scores': {'base': Value('float64'), 'reason': Value('float64'), 'reason+fetch': Value('float64'), 'rag-deep': Value('float64'), 'reason+kb': Value('float64'), 'reason+verify': Value('float64')}, 'val_traces': Value('int64'), 'considered': List(Value('string')), 'winner_spec': {'label': Value('string'), 'reasoning': Value('bool'), 'knowledge': Value('bool'), 'verify': Value('bool'), 'grounder': Value('string'), 'workspace': Value('bool'), 'top_k': Value('null'), 'demo_obs_cap': Value('null')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              key_mode: string
              winner_spec: struct<label: string, reasoning: bool, knowledge: bool, verify: bool, grounder: string, workspace: b (... 37 chars omitted)
                child 0, label: string
                child 1, reasoning: bool
                child 2, knowledge: bool
                child 3, verify: bool
                child 4, grounder: string
                child 5, workspace: bool
                child 6, top_k: null
                child 7, demo_obs_cap: null
              considered: list<item: string>
                child 0, item: string
              val_traces: int64
              winner_label: string
              scores: struct<base: double, reason: double, reason+fetch: double, rag-deep: double, reason+kb: double, reas (... 18 chars omitted)
                child 0, base: double
                child 1, reason: double
                child 2, reason+fetch: double
                child 3, rag-deep: double
                child 4, reason+kb: double
                child 5, reason+verify: double
              to
              {'winner_label': Value('string'), 'scores': {'base': Value('float64'), 'reason': Value('float64'), 'reason+fetch': Value('float64'), 'rag-deep': Value('float64'), 'reason+kb': Value('float64'), 'reason+verify': Value('float64')}, 'val_traces': Value('int64'), 'considered': List(Value('string')), 'winner_spec': {'label': Value('string'), 'reasoning': Value('bool'), 'knowledge': Value('bool'), 'verify': Value('bool'), 'grounder': Value('string'), 'workspace': Value('bool'), 'top_k': Value('null'), 'demo_obs_cap': Value('null')}}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

terminal-tasks — real agent-environment traces

Computer-use agent runs in real terminal containers: bash commands and their true outputs from live task environments.

Every trace is a REAL run: an LLM agent stepping against the actual benchmark environment, with each transition (tool call → true environment observation) recorded as OpenTelemetry GenAI spans (traces.otel.jsonl, one span per line). Captured by world-model-harness's environment-capture package, which also holds the adapter, capture scripts, and per-corpus provenance: see packages/environment-capture/terminal-tasks/.

License and attribution

Derived from terminal-bench (Apache 2.0); this corpus is redistributed under the same terms (apache-2.0). The trace text embeds task data and environment output from the upstream benchmark — keep this attribution if you redistribute.

traces.otel.jsonl — the trace corpus (OTel GenAI spans, one JSON object per line)

Using it

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "experiential-labs/wmh-terminal-tasks-traces", "traces.otel.jsonl", repo_type="dataset"
)

or, from a world-model-harness checkout:

uv run wmh download terminal-tasks

Downloads last month: 80

Total file size:

6.01 MB

terminal-tasks — real agent-environment traces

License and attribution

Contents

Using it