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KanakMalpani
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loopnet-v0.3-preview

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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<id: string, role: string, model: struct<provider: string, name: string, temperature: double>, inputs: list<item: struct<from: string>>, outputs: struct<name: string, format: string>, timeout_seconds: int64, cost_budget_usd: double, depends_on: list<item: string>>
to
{'id': Value('string'), 'role': Value('string'), 'model': {'provider': Value('string'), 'name': Value('string'), 'temperature': Value('float64')}, 'inputs': List({'from': Value('string')}), 'outputs': {'name': Value('string'), 'format': Value('string')}, 'timeout_seconds': Value('int64'), 'retries': Value('int64'), 'cost_budget_usd': Value('float64')}
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 478, 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 2303, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<id: string, role: string, model: struct<provider: string, name: string, temperature: double>, inputs: list<item: struct<from: string>>, outputs: struct<name: string, format: string>, timeout_seconds: int64, cost_budget_usd: double, depends_on: list<item: string>>
              to
              {'id': Value('string'), 'role': Value('string'), 'model': {'provider': Value('string'), 'name': Value('string'), 'temperature': Value('float64')}, 'inputs': List({'from': Value('string')}), 'outputs': {'name': Value('string'), 'format': Value('string')}, 'timeout_seconds': Value('int64'), 'retries': Value('int64'), 'cost_budget_usd': Value('float64')}

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Check out the documentation for more information.

LoopNet

Ground truth for self-improving systems.

Structured loop designs, execution trajectories, outcomes, and failure modes — so you can train, evaluate, and debug loops with data, not anecdotes.


CI Code: MIT Dataset: CC BY 4.0 Records Failure rate Hugging Face v0.2 Hugging Face v0.1


Load the dataset · End-to-end tutorial · Contribute records · Data card

Why this exists

Computer vision had ImageNet. RL had MuJoCo. Loop engineering had no shared corpus.

LoopNet fills that gap: every record is a complete loop story — spec, trajectory, outcome, LES breakdown, and when things break, a fail.* code from the shared taxonomy.

What you can do with it

Use case How LoopNet helps
Failure prediction 42% labeled failures — models learn what breaking looks like
Benchmark generalization Same schema as LoopBench holdout (v0.2)
Zero-cost replay Feed LoopGym ReplayEnv — no API spend
Research & fine-tuning JSONL + Parquet + Hugging Face — train on loop structure, not chat logs
Community contributions ln/record-v1 + submission guide

Corpus at a glance (v0.2)

Records 545 (500 seed + 45 captured LoopGym runs)
Failure rate 40% — meets corpus policy
Schema ln/record-v1 · pins lss@1.0.0 + les@1.0.0
Source Synthetic seed + SimEnv captures (3 LoopBench envs)
License Code MIT · Dataset CC BY 4.0

Version registry: ECOSYSTEM_VERSIONS.md. Seed-only v0.1 (500 records) remains at loopnet-seed-v0.1.

Load in one minute

Hugging Face (recommended — v0.2):

from datasets import load_dataset

ds = load_dataset("KanakMalpani/loopnet-v0.2", split="train")
print(ds[0]["outcome"], ds[0]["pattern_slug"])

Seed-only v0.1:

ds = load_dataset("KanakMalpani/loopnet-seed-v0.1", split="train")

Stream from GitHub (no clone):

ds = load_dataset(
    "json",
    data_files="https://raw.githubusercontent.com/KanakMalpani/loopnet/main/data/seed/records.jsonl",
    split="train",
)

Replay in LoopGym:

import loopgym as lg

env = lg.make("replay/loopnet-v1")
obs = env.reset(record_id="ln-00042")  # trajectory from corpus

Validate and reproduce

Explored this corpus? Post on the reproduction challenge after REPRODUCE.md.

Where it sits 

flowchart LR
  CORE[Loop Core Engineering]
  NET["<b>LoopNet</b><br/>you are here"]
  GYM[LoopGym ReplayEnv]
  BENCH[LoopBench holdout]

  CORE --> NET
  NET --> GYM
  NET -.-> BENCH
Layer Repo
Specs & failure codes Loop Core Engineering
Dataset LoopNet
Execution LoopGym
Observability loop-observability
Public scores LoopBench

Repository map

Path Purpose
schema/loopnet-record-v1.json Canonical record schema
data/seed/records.jsonl Seed corpus
scripts/validate_record.py Schema + policy validation
scripts/generate_seed.py Deterministic regeneration (--seed 42)
guides/COMMUNITY-SUBMISSION.md Contribute records via PR
submissions/community/ Community JSONL inbox

Citation 

@dataset{loopnet_v02,
  title={LoopNet v0.2},
  author={Malpani, Kanak},
  year={2026},
  url={https://huggingface.co/datasets/KanakMalpani/loopnet-v0.2}
}
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