Datasets:

banyaaiofficial
/

prototypebench-v1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
base_commit: string
contamination_tier: string
created_at: timestamp[s]
environment: struct<bun_lock_sha: string, docker_compose_sha: string, node_version: string, python_version: strin (... 23 chars omitted)
  child 0, bun_lock_sha: string
  child 1, docker_compose_sha: string
  child 2, node_version: string
  child 3, python_version: string
  child 4, uv_lock_sha: string
fail_to_pass: struct<backend: list<item: string>, frontend: list<item: null>>
  child 0, backend: list<item: string>
      child 0, item: string
  child 1, frontend: list<item: null>
      child 0, item: null
head_commit: string
instance_id: string
notes: string
pass_to_pass: struct<backend: list<item: string>, frontend: list<item: null>>
  child 0, backend: list<item: string>
      child 0, item: string
  child 1, frontend: list<item: null>
      child 0, item: null
patch: string
pr_author: string
pr_labels: list<item: string>
  child 0, item: string
pr_number: int64
pr_title: string
pr_url: string
problem_statement: string
repo: string
schema_version: string
stack_domain: string
test_patch: string
test_patch_backend: string
test_patch_frontend: string
to
{'instance_id': Value('string'), 'repo': Value('string'), 'pr_number': Value('int32'), 'pr_url': Value('string'), 'pr_title': Value('string'), 'base_commit': Value('string'), 'head_commit': Value('string'), 'problem_statement': Value('string'), 'patch': Value('string'), 'test_patch': Value('string'), 'stack_domain': Value('string'), 'contamination_tier': Value('string'), 'created_at': Value('string'), 'schema_version': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2281, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2227, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              base_commit: string
              contamination_tier: string
              created_at: timestamp[s]
              environment: struct<bun_lock_sha: string, docker_compose_sha: string, node_version: string, python_version: strin (... 23 chars omitted)
                child 0, bun_lock_sha: string
                child 1, docker_compose_sha: string
                child 2, node_version: string
                child 3, python_version: string
                child 4, uv_lock_sha: string
              fail_to_pass: struct<backend: list<item: string>, frontend: list<item: null>>
                child 0, backend: list<item: string>
                    child 0, item: string
                child 1, frontend: list<item: null>
                    child 0, item: null
              head_commit: string
              instance_id: string
              notes: string
              pass_to_pass: struct<backend: list<item: string>, frontend: list<item: null>>
                child 0, backend: list<item: string>
                    child 0, item: string
                child 1, frontend: list<item: null>
                    child 0, item: null
              patch: string
              pr_author: string
              pr_labels: list<item: string>
                child 0, item: string
              pr_number: int64
              pr_title: string
              pr_url: string
              problem_statement: string
              repo: string
              schema_version: string
              stack_domain: string
              test_patch: string
              test_patch_backend: string
              test_patch_frontend: string
              to
              {'instance_id': Value('string'), 'repo': Value('string'), 'pr_number': Value('int32'), 'pr_url': Value('string'), 'pr_title': Value('string'), 'base_commit': Value('string'), 'head_commit': Value('string'), 'problem_statement': Value('string'), 'patch': Value('string'), 'test_patch': Value('string'), 'stack_domain': Value('string'), 'contamination_tier': Value('string'), 'created_at': Value('string'), 'schema_version': Value('string')}
              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.

YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

PrototypeBench v0.1

Can your agent ship a full-stack AI-native prototype?

PrototypeBench is an open benchmark for evaluating AI coding agents on full-stack feature shipping. Where SWE-Bench measures bug-fixing in mature Python libraries, PrototypeBench measures "can the agent ship a full-stack feature on a modern AI-native stack?"

Project home: https://github.com/prototypebench/prototypebench
Website: https://prototypebench.org
License: MIT
Version: v0.1 (initial corpus)
Language: English (problem statements), Python (backend code), TypeScript/JavaScript (frontend code, future)

Dataset Summary

71 PR-mined task instances from active open-source repositories, each shaped for SWE-Bench-compatible execution-based scoring:

Stat	Value
Total instances	71
Sources	2 (`fastapi/full-stack-fastapi-template`, `IBM/mcp-context-forge`)
`FAIL_TO_PASS` tests	689
`PASS_TO_PASS` regression-guard tests	31,644
Total test cases per full eval	32,333
stack_domain	71 backend_only (v0.1); frontend & fullstack in later versions
contamination_tier	71 held_out (all post-2026-01-01)
Schema version	0.1

Comparison: SWE-Bench Verified has 500 instances, SWE-Bench Lite 300, HumanEval 164. v1 public-beta targets 200–300.

Scoring

Execution-based binary scoring (no LLM-as-judge):

score(instance) = 1  iff  FAIL_TO_PASS ⊆ passing_tests
                     AND  PASS_TO_PASS ⊆ passing_tests    (no regression)
                  0  otherwise

Judge: pytest (backend) and Playwright (frontend, future). Ground truth = the actual merged PR diff (hidden from the agent). See the methodology notes.

Usage

from datasets import load_dataset

ds = load_dataset("banyaaiofficial/prototypebench-v1", split="test")

for item in ds:
    print(item["instance_id"])           # e.g. "IBM__mcp-context-forge-4270"
    print(item["problem_statement"])     # NL task spec (PR body or closing issue)
    base_sha = item["base_commit"]        # pre-PR commit — agent starts here
    # Agent produces a non-test unified diff against base_sha.
    # Score it with the companion harness:
    #   pbench score --source <short> --pr <N> --patch-file agent_patch.diff

Each instance extends the SWE-Bench instances.jsonl schema with dual-test fields (fail_to_pass.backend / .frontend, test_patch_backend / .frontend) for future Playwright integration.

Full schema: https://github.com/prototypebench/prototypebench/blob/main/schemas/task_instance.schema.json

Source Composition

Source	Stars	License	Instances	F2P	P2P
`fastapi/full-stack-fastapi-template`	42.7k	MIT	3	7	77
`IBM/mcp-context-forge`	3.6k	Apache-2	68	682	31,567

All PRs are merged PRs with maintainer-reviewed tests. Task instances mine the natural atomic unit of change (one feature or fix at a time).

Data Fields

See the task-schema doc for full field-by-field semantics. Highlights:

instance_id — stable unique ID (owner__repo-<pr_number>)
base_commit / head_commit — SHAs bounding the reference change
problem_statement — natural-language task spec (from closing issue body, else PR description)
patch — reference solution (non-test diff). Hidden from the agent at evaluation time.
test_patch — test-only diff that the harness applies before running the agent's patch
fail_to_pass — {backend: [...], frontend: [...]} — tests the agent must make pass
pass_to_pass — {backend: [...], frontend: [...]} — regression-guard tests (must not break)
stack_domain — backend_only | frontend_only | fullstack
environment — python_version, node_version, uv_lock_sha, etc. for reproducible builds
contamination_tier — public | held_out | internal_only

Contamination & Fairness

Held-out by construction: all v0.1 instances are merged after 2026-01-01 (Claude Opus 4.7 cutoff). Submitters must disclose their model cutoff for point-count adjustment.
Rotation: held-out tier is rotated per leaderboard season (Phase 5).
No vendor branding: benchmark carries no vendor name. Hosted on banyaaiofficial for convenience only; the benchmark is project-neutral.

Limitations

v0.1 is backend-only (no Playwright scoring yet — the harness supports it but frontend-kind PRs are v1+).
mcp-context-forge 68 instances dominate the corpus — diverse workload coverage is a v1+ priority.
"test strength = benchmark quality": PRs with weak tests are filtered but not perfectly. Curator review recommended.
Execution-based scoring requires running tests (not instantaneous) — see the harness for Docker-based reproducible runs.

Related Benchmarks

SWE-Bench — Python library bug-fixes (2,294 instances). PrototypeBench extends the pattern to modern AI-native full-stack apps.
SWE-Bench Lite / Verified — curated subsets.
Terminal-Bench — CLI tasks.
BigCodeBench — library-usage function-level tasks.

Citation

Citation format will be fixed at Phase 4 public launch. For now:

@misc{prototypebench_v01,
  title        = {PrototypeBench v0.1: An AI-native Full-Stack Coding Agent Benchmark},
  year         = {2026},
  url          = {https://github.com/prototypebench/prototypebench},
  note         = {71 instances across 2 source repos; execution-based scoring}
}

Changelog

v0.1 (2026-04-20): initial corpus. 71 backend_only instances, all held_out. Schema v0.1.

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