Datasets:
Dataset Viewer
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
schema_version: string
task_name: string
agent: struct<name: string, version: string, model_name: string>
child 0, name: string
child 1, version: string
child 2, model_name: string
steps: list<item: struct<step: int64, source: string, message: string, reasoning_content: string, tool_call (... 228 chars omitted)
child 0, item: struct<step: int64, source: string, message: string, reasoning_content: string, tool_calls: list<ite (... 216 chars omitted)
child 0, step: int64
child 1, source: string
child 2, message: string
child 3, reasoning_content: string
child 4, tool_calls: list<item: struct<name: string, arguments: struct<command: string>>>
child 0, item: struct<name: string, arguments: struct<command: string>>
child 0, name: string
child 1, arguments: struct<command: string>
child 0, command: string
child 5, observation: struct<results: list<item: struct<content: string>>>
child 0, results: list<item: struct<content: string>>
child 0, item: struct<content: string>
child 0, content: string
child 6, metrics: struct<prompt_tokens: int64, completion_tokens: int64>
child 0, prompt_tokens: int64
child 1, completion_tokens: int64
child 7, llm_call_count: int64
final_metrics: struct<total_prompt_tokens: int64, total_completion_tokens: int64, total_steps: int64, peak_context_ (... 14 chars omitted)
child 0, total_prompt_tokens: int64
child 1, total_completion_tokens: int64
child 2, total_steps: int64
child 3, peak_context_tokens: int64
durations_seconds: struct<agent_execution: double, agent_setup: double, environment_setup: double, total: double, verif (... 12 chars omitted)
child 0, agent_execution: double
child 1, agent_setup: double
child 2, environment_setup: double
child 3, total: double
child 4, verifier: double
exception: struct<>
status: string
verifier: struct<>
trajectory_metrics: struct<assistant_turns: int64, completion_tokens: int64, peak_context_tokens: int64, prompt_tokens: (... 39 chars omitted)
child 0, assistant_turns: int64
child 1, completion_tokens: int64
child 2, peak_context_tokens: int64
child 3, prompt_tokens: int64
child 4, steps: int64
child 5, tool_calls: int64
artifacts: struct<patch_bytes: int64, patch_empty: bool, patch_omitted: bool, patch_omitted_reason: string, pat (... 113 chars omitted)
child 0, patch_bytes: int64
child 1, patch_empty: bool
child 2, patch_omitted: bool
child 3, patch_omitted_reason: string
child 4, patch_present: bool
child 5, patch_redacted: bool
child 6, patch_sha256: null
child 7, patch_source_present: bool
child 8, trajectory_present: bool
verifier_present: bool
run_id: string
to
{'agent': {'model': Value('string'), 'name': Value('string'), 'version': Value('string')}, 'artifacts': {'patch_bytes': Value('int64'), 'patch_empty': Value('bool'), 'patch_omitted': Value('bool'), 'patch_omitted_reason': Value('string'), 'patch_present': Value('bool'), 'patch_redacted': Value('bool'), 'patch_sha256': Value('null'), 'patch_source_present': Value('bool'), 'trajectory_present': Value('bool')}, 'durations_seconds': {'agent_execution': Value('float64'), 'agent_setup': Value('float64'), 'environment_setup': Value('float64'), 'total': Value('float64'), 'verifier': Value('float64')}, 'exception': {}, 'run_id': Value('string'), 'schema_version': Value('string'), 'status': Value('string'), 'task_name': Value('string'), 'trajectory_metrics': {'assistant_turns': Value('int64'), 'completion_tokens': Value('int64'), 'peak_context_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'steps': Value('int64'), 'tool_calls': Value('int64')}, 'verifier': {}, 'verifier_present': Value('bool')}
because column names don't match
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 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
schema_version: string
task_name: string
agent: struct<name: string, version: string, model_name: string>
child 0, name: string
child 1, version: string
child 2, model_name: string
steps: list<item: struct<step: int64, source: string, message: string, reasoning_content: string, tool_call (... 228 chars omitted)
child 0, item: struct<step: int64, source: string, message: string, reasoning_content: string, tool_calls: list<ite (... 216 chars omitted)
child 0, step: int64
child 1, source: string
child 2, message: string
child 3, reasoning_content: string
child 4, tool_calls: list<item: struct<name: string, arguments: struct<command: string>>>
child 0, item: struct<name: string, arguments: struct<command: string>>
child 0, name: string
child 1, arguments: struct<command: string>
child 0, command: string
child 5, observation: struct<results: list<item: struct<content: string>>>
child 0, results: list<item: struct<content: string>>
child 0, item: struct<content: string>
child 0, content: string
child 6, metrics: struct<prompt_tokens: int64, completion_tokens: int64>
child 0, prompt_tokens: int64
child 1, completion_tokens: int64
child 7, llm_call_count: int64
final_metrics: struct<total_prompt_tokens: int64, total_completion_tokens: int64, total_steps: int64, peak_context_ (... 14 chars omitted)
child 0, total_prompt_tokens: int64
child 1, total_completion_tokens: int64
child 2, total_steps: int64
child 3, peak_context_tokens: int64
durations_seconds: struct<agent_execution: double, agent_setup: double, environment_setup: double, total: double, verif (... 12 chars omitted)
child 0, agent_execution: double
child 1, agent_setup: double
child 2, environment_setup: double
child 3, total: double
child 4, verifier: double
exception: struct<>
status: string
verifier: struct<>
trajectory_metrics: struct<assistant_turns: int64, completion_tokens: int64, peak_context_tokens: int64, prompt_tokens: (... 39 chars omitted)
child 0, assistant_turns: int64
child 1, completion_tokens: int64
child 2, peak_context_tokens: int64
child 3, prompt_tokens: int64
child 4, steps: int64
child 5, tool_calls: int64
artifacts: struct<patch_bytes: int64, patch_empty: bool, patch_omitted: bool, patch_omitted_reason: string, pat (... 113 chars omitted)
child 0, patch_bytes: int64
child 1, patch_empty: bool
child 2, patch_omitted: bool
child 3, patch_omitted_reason: string
child 4, patch_present: bool
child 5, patch_redacted: bool
child 6, patch_sha256: null
child 7, patch_source_present: bool
child 8, trajectory_present: bool
verifier_present: bool
run_id: string
to
{'agent': {'model': Value('string'), 'name': Value('string'), 'version': Value('string')}, 'artifacts': {'patch_bytes': Value('int64'), 'patch_empty': Value('bool'), 'patch_omitted': Value('bool'), 'patch_omitted_reason': Value('string'), 'patch_present': Value('bool'), 'patch_redacted': Value('bool'), 'patch_sha256': Value('null'), 'patch_source_present': Value('bool'), 'trajectory_present': Value('bool')}, 'durations_seconds': {'agent_execution': Value('float64'), 'agent_setup': Value('float64'), 'environment_setup': Value('float64'), 'total': Value('float64'), 'verifier': Value('float64')}, 'exception': {}, 'run_id': Value('string'), 'schema_version': Value('string'), 'status': Value('string'), 'task_name': Value('string'), 'trajectory_metrics': {'assistant_turns': Value('int64'), 'completion_tokens': Value('int64'), 'peak_context_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'steps': Value('int64'), 'tool_calls': Value('int64')}, 'verifier': {}, 'verifier_present': Value('bool')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DeepSWE 1.1 trajectories: Qwen3.8-27B agents and baselines
This dataset contains agent trajectories and evaluation results from 7 complete runs on DeepSWE 1.1. The main experiments evaluate Qwen3.8-27B through Mini-SWE, Claude Code, and Pi. Muse-Glimmer-30B and Qwen3.6-27B are included as weaker reference baselines.
Every run covers all 113 benchmark tasks. Altogether, the dataset contains:
- 791 task-level result records;
- 791 compressed agent trajectories;
- 425 submitted text patches;
- exact run configurations, aggregate scores, and efficiency statistics.
This repository contains evaluation outputs. The tasks, environments, and verifiers live in the separate DeepSWE repository.
What is DeepSWE?
DeepSWE measures coding agents on 113 original, long-horizon software-engineering tasks drawn from active TypeScript, Go, Python, JavaScript, and Rust projects. For each task, an agent receives an isolated repository and a natural-language request. It must inspect the code, implement the requested behavior, run tests, and submit a patch. DeepSWE then applies that patch to a clean copy and grades it with held-out tests.
Results
| Model | Agent | Reasoning | F2P (%) | Reward (%) | Solved | Execution errors |
|---|---|---|---|---|---|---|
| Muse-Glimmer-30B | Mini-SWE | xhigh | 39.43 | 5.31 | 6/113 | 19 |
| Qwen3.6-27B | Pi | thinking on | 63.44 | 3.54 | 4/113 | 1 |
| Qwen3.8-27B | Mini-SWE | xhigh | 77.42 | 41.59 | 47/113 | 0 |
| Qwen3.8-27B | Claude Code | xhigh | 88.52 | 42.48 | 48/113 | 0 |
| Qwen3.8-27B | Pi | low | 86.47 | 39.82 | 45/113 | 0 |
| Qwen3.8-27B | Pi | medium | 84.10 | 43.36 | 49/113 | 1 |
| Qwen3.8-27B | Pi | xhigh | 86.65 | 46.02 | 52/113 | 3 |
An execution error means that the agent phase ended with an error. It is not the
number of tasks receiving zero reward. Exact model, sampling, context-window,
output-limit, timeout, retry, and agent-version settings are available through
the linked run.json files.
Repository layout
README.md
summary/
main-results.csv
efficiency-quantiles.csv
runs/
<run-id>/
run.json
tasks/
<task-id>/
result.json
trajectory.json.gz
model.patch # when available
run.json contains the run configuration, aggregate scores, efficiency
distributions, and an index of all tasks. result.json contains one task's
scores, status, durations, usage metrics, and artifact availability.
trajectory.json.gz contains the task prompt, assistant messages, reasoning
trace, tool calls, tool outputs, and usage information. Message details vary
slightly between agent interfaces.
model.patch is available for 425 tasks. Patch availability is recorded in
each task's result.json.
Loading the data
Download a local snapshot:
from pathlib import Path
from huggingface_hub import snapshot_download
root = Path(snapshot_download(
repo_id="kaitchup/DeepSWE1.1-trajectories-Qwen3.8-27B",
repo_type="dataset",
))
Read the result table and one trajectory:
import csv
import gzip
import json
with (root / "summary" / "main-results.csv").open() as file:
runs = list(csv.DictReader(file))
run_dir = root / "runs" / "qwen3.8-27b-pi-xhigh"
task_dir = sorted((run_dir / "tasks").iterdir())[0]
result = json.loads((task_dir / "result.json").read_text())
with gzip.open(task_dir / "trajectory.json.gz", "rt", encoding="utf-8") as file:
trajectory = json.load(file)
print(result["task_name"], result["verifier"])
print("trajectory steps:", len(trajectory["steps"]))
Limitations
- Each configuration has one run, so small differences should not be treated as statistically significant without repetitions.
- Agent interfaces differ in prompting, tools, context management, retry policy, and output limits. This is not a controlled model-only comparison.
low,medium, andxhighare configuration labels, not standardized amounts of inference compute across different agents or models.- Token and turn counts come from different agent adapters and may not be perfectly comparable.
Acknowledgments
Verda provided the RTX Pro 6000s and H200s used to run these experiments.
Verda is a full-stack AI cloud built for high-performance inference, training, and agentic workloads, with data privacy and sustainability at its core.
License and third-party code
This dataset is released under Apache-2.0. The benchmark tasks use third-party open-source projects under their respective licenses. Trajectories and patches can contain excerpts from or modifications to those projects; those materials remain subject to their applicable upstream licenses.
See DeepSWE's project-level provenance table.
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