Roman1111111/opus-gpt-swe-frontier-core

SWE Base

Repository-level software engineering trajectories for training coding agents.

2,459 chat trajectories · 48,499 API calls · $837.57 recorded generation cost

SWE-bench · debugging · patching · tools · agents

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Overview

SWE Base is a software-engineering dataset centered on real repository issues. Each training example gives an agent a problem statement and captures the multi-turn process of inspecting a codebase, reasoning about a bug, using development tools, editing files, running tests, and working toward a patch.

The collection is designed for supervised fine-tuning and research on coding agents that need to operate across complete repositories rather than answer isolated programming questions.

What's inside

Capability	Examples include
Repository exploration	Searching files, reading code, and locating relevant components
Bug diagnosis	Connecting issue reports to implementation behavior
Patch generation	Making targeted changes across one or more files
Tool use	Shell commands, test runners, and iterative environment interaction
Verification	Running tests and interpreting failures or regressions
Long-horizon reasoning	Multi-step trajectories from issue description to attempted resolution

Tasks span widely used open-source Python projects represented in SWE-bench, including Django, SymPy, scikit-learn, Matplotlib, pytest, Sphinx, Astropy, and others. Additional SWE-bench Pro artifacts broaden coverage to larger and more varied repositories.

Training-ready chat data

Config	Rows	Resolved	Resolution rate	API calls	Recorded cost
small	500	371	74.20%	8,134	$129.19
medium	1,959	855	43.64%	40,365	$708.38
Total	2,459	1,226	49.86%	48,499	$837.57

Resolution statistics are taken from the stored resolved field. They describe these recorded runs and should not be interpreted as official model benchmark scores.

Format

The small and medium configurations contain one conversation per JSONL row:

{
  "instance_id": "project__repository-12345",
  "problem_statement": "Repository issue description...",
  "messages": [
    {"role": "system", "content": "Agent instructions..."},
    {"role": "user", "content": "Issue and environment state..."},
    {"role": "assistant", "content": "Analysis and tool action..."}
  ],
  "model": "generator identifier",
  "resolved": true,
  "instance_cost": 0.12,
  "api_calls": 14
}

Field	Type	Description
instance_id	string	SWE-bench task identifier
problem_statement	string	Repository issue to solve
messages	list	Ordered agent/environment conversation
model	string	Recorded generator identifier
resolved	bool	Whether the stored run resolved the task
instance_cost	float	Recorded API cost for the run
api_calls	int	Number of API calls in the run

Environment observations can appear as user messages, so the role sequence may differ from a conventional two-party chat transcript.

Load with 🤗 Datasets

After uploading this folder to the Hub:

from datasets import load_dataset

small = load_dataset("YOUR_USERNAME/swe-base", "small", split="train")
medium = load_dataset("YOUR_USERNAME/swe-base", "medium", split="train")

print(medium[0]["instance_id"])
print(medium[0]["messages"])

Load the local files directly:

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files="gpt5.2swe-medium.jsonl",
    split="train",
)

Raw trajectory artifacts

The repository also contains raw SWE-bench Verified and SWE-bench Pro artifacts from multiple agent systems:

File	Artifact rows
claude-sonnet-4-5_swebench_pro_traj.jsonl	733
claude-sonnet-4-5_swebench_verified_traj.jsonl	502
gemini_3_pro_swebench_verified_traj.jsonl	502
gpt-5_swebench_verified_traj.jsonl	502
gpt-5-2_swebench_verified_traj.jsonl	501
gpt-5-mini_swebench_verified_traj.jsonl	502
kimi-k2-thinking_swebench_verified_traj.jsonl	501

These are preservation-oriented artifacts, not normalized chat splits. Rows may contain per-instance trajectories, patches, evaluation outputs, or aggregate result maps. Inspect and normalize their structures before using them for training.

Model names in filenames identify the recorded trajectory generators. No model weights are included.

Intended use

SWE Base is suitable for research and experimentation involving:

• supervised fine-tuning of repository-level coding agents;
• tool-use and terminal-action prediction;
• debugging and patch-generation research;
• resolved-versus-unresolved trajectory analysis;
• process supervision and long-context agent behavior;
• trajectory filtering, ranking, and curriculum design.

Limitations

• A resolved label does not guarantee that every intermediate action is optimal or desirable.
• Unresolved trajectories may contain useful negative examples but require intentional handling during training.
• Agent messages can include verbose reasoning, failed commands, repeated attempts, and environment-specific paths.
• Tool and message conventions can vary across trajectory generators.
• Recorded costs reflect the stored runs and may not reproduce current API pricing.
• Benchmark contamination and overfitting are important risks when training on benchmark trajectories.
• No security, secrets, license, or personally identifiable information audit is claimed.

Evaluate and filter the data for the target model, chat template, and deployment context.

License

No license is declared in this card. Review the repository terms and the licenses of the represented codebases and benchmark materials before redistribution or commercial use.

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Built for agents that read the repo, find the bug, make the patch, and run the tests.