datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SWE-smith
SWE-smith Dataset
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[12/14/2025] NOTE: We will no longer actively update this dataset.
While this dataset is still functional and usable, we recommend you use the `SWE-bench/SWE-smith-[lang]` datasets.
For better maintainability and ease-of-use, we are maintaining language-specific datasets in lieu of this mono-repo.
The SWE-smith Dataset is a training dataset of 50137 task instances from 128 GitHub repositories, collected using the SWE-smith toolkit.… See the full description on the dataset page: https://huggingface.co/datasets/SWE-bench/SWE-smith.SWE-smith-py
SWE-smith Dataset
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As of 12/14/2025, SWE-smith: Python contains 50908 task instances from 131 GitHub repositories
The SWE-smith Dataset is the largest open source dataset for training software engineering agents.
All SWE-smith task instances come with an executable environment.
To learn more about how to use this dataset to train Language Models for Software Engineering, please refer to the documentation.
SWE-smith-trajectories
SWE-smith Trajectories
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This dataset contains the 5017 trajectories we fine-tuned Qwen 2.5 Coder Instruct on, leading to
SWE-agent-LM-32B, a coding LM agent that
achieve 40.2% on SWE-bench Verified (no verifiers or multiple rollouts, just 1 attempt per instance).
Trajectories were generated by running SWE-agent + Claude 3.7 Sonnet on task instances from
the SWE-smith dataset.
SWE-smith-go
SWE-smith Dataset
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As of 12/14/202, SWE-smith: Golang contains 8212 task instances from 87 GitHub repositories
The SWE-smith Dataset is the largest open source dataset for training software engineering agents.
All SWE-smith task instances come with an executable environment.
To learn more about how to use this dataset to train Language Models for Software Engineering, please refer to the documentation.
harbor-swesmith-rl-artifacts
Harbor SWE-Smith 强化学习数据产物
本数据集是 Harbor Qwen 工具调用代码智能体强化学习项目使用的冻结任务集，服务于 GRPO、原生价值模型/GAE PPO、训练过程诊断和统一协议评测。
项目已于 2026 年 8 月 30 日完成 P0 评测并进入阶段性归档。本数据集用于保留实验所依赖的数据切分、任务执行文件和审计信息，不代表新的通用代码能力基准。
数据概况
切分
任务数
训练集
187
验证集
42
测试集
38
合计
267
数据覆盖 89 个上游代码仓库。三个切分之间同时执行任务标识和仓库级隔离检查。
正式数据集名称：
swesmith-curated-grpo-267-v1
冻结切分的语义摘要：
ae5df9a3f4a3fc8af44fac420b36529e283839e1bd3de9daba65d5bcda51447d
该值来自 split-manifest.json 的 sha256 字段，用于标识切分语义，不等同于该文件本身的字节级… See the full description on the dataset page: https://huggingface.co/datasets/keryszhan/harbor-swesmith-rl-artifacts.SWE-Smith
A extended version of the original SWE-smith-py dataset with more problem descriptions!
claude-code-glm53-swesmith-trajectories
Claude-Code-native Coding Agent Teacher Trajectories (GLM-5.3 × SWE-smith)
English | 简体中文
A private research archive of execution-verified, multi-turn coding-agent trajectories.
A strong teacher (GLM-5.3) drives a real coding-agent harness (Claude Code) inside
verified Docker environments derived from SWE-smith tasks; every trajectory is graded
in a clean verifier container against the task's exact FAIL_TO_PASS / PASS_TO_PASS tests.
⚠️ PRIVATE dataset. Raw wire traces contain… See the full description on the dataset page: https://huggingface.co/datasets/liangzhidanta/claude-code-glm53-swesmith-trajectories.swesmith-qwen3.6-35b-a3b
SWE-smith trajectories from Qwen3.6-35B-A3B
Multi-turn coding-agent trajectories (issue → tool-using rollout → patch) produced by
Qwen3.6-35B-A3B on SWE-smith tasks, stored untokenized.
This is the exact SFT corpus used for the harbor arm of the
nanoswe teacher-distillation experiments.
101,901 trajectories over 45,242 unique SWE-smith task instances (3 sampled rollouts
per task, ~2.25 surviving filtering), 53 parquet shards, ~1.4 GB.
≈1.96B training tokens = exactly one epoch… See the full description on the dataset page: https://huggingface.co/datasets/nanoswe/swesmith-qwen3.6-35b-a3b.swe_smith_rebenchv2_5136
SWE-smith + SWE-rebench V2 5136 Mix
This dataset is the swe_smith_rebenchv2_5136 training mix used by the rLLM SWE training scripts. It combines filtered SWE-smith trajectory tasks with sampled SWE-rebench V2 tasks so future training jobs can pull the prepared parquet directly instead of regenerating it with the long preparation script.
Contents
data/train.parquet: the canonical rLLM task rows, 5,136 examples.
rllm_verl/train.parquet: the rLLM DatasetRegistry… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/swe_smith_rebenchv2_5136.SWE-smith-trajectories
SWE-smith Trajectories
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This dataset contains the 5017 trajectories we fine-tuned Qwen 2.5 Coder Instruct on, leading to
SWE-agent-LM-32B, a coding LM agent that
achieve 40.2% on SWE-bench Verified (no verifiers or multiple rollouts, just 1 attempt per instance).
Trajectories were generated by running SWE-agent + Claude 3.7 Sonnet on task instances from
the SWE-smith dataset.
SWE-smith-code-search
SWE-smith Code Search
SWE-smith Code Search is a code-localization training dataset derived from
SWE-bench/SWE-smith-py.
It converts each eligible SWE-smith mutation patch into structured file,
class/module, and function/method targets. The release contains 39,291 examples
from 131 Python repositories and is intended for training and evaluating coding
agents that must locate relevant code before editing it.
中文简介：本数据集从 SWE-smith Python 原始任务中提取文件、类/模块、函数/方法三级
代码定位标签，可直接用于 Code… See the full description on the dataset page: https://huggingface.co/datasets/LeeXugar/SWE-smith-code-search.swe-smith-frozen-trajectories-openai
SWE-Smith Frozen Trajectories — OpenAI Wire Format
This dataset is the OpenAI chat-completions wire-format release of
reflectio/swe-smith-frozen-trajectories,
derived from the tool split of
SWE-bench/SWE-smith-trajectories.
It is a serving-performance workload for realistic multi-turn coding-agent
histories. It can be used to measure request throughput, input/output token
throughput, TTFT, TPOT, streaming behavior, and prefix-cache reuse. It is not
a coding-correctness… See the full description on the dataset page: https://huggingface.co/datasets/reflectio/swe-smith-frozen-trajectories-openai.swe-smith-frozen-trajectories
SWE-Smith Frozen Trajectories
This dataset is a serving-performance workload derived from the tool split of
SWE-bench/SWE-smith-trajectories.
It is designed for measuring throughput, request rate, time to first token,
inter-token latency, and prefix-cache behavior with realistic multi-turn coding
agent histories.
It is not a coding-correctness benchmark. The tested model's responses are
not executed or scored.
Processing
Keep trajectories generated by… See the full description on the dataset page: https://huggingface.co/datasets/reflectio/swe-smith-frozen-trajectories.agent-trajectories-swesmith-random-subset
Agent Trajectories: SWE-Smith Random Subset — Single Model
Summary
Full multi-turn agent trajectories collected from a random subset of SWE-Smith issues,
generated with a single model per trajectory (no branching or lookahead).
Contains a resolved column from SWE-bench evaluation.
Intended for SFT of agent models on diverse, synthetically generated coding tasks.
Data Collection
Each trajectory was produced by a mini-swe-agent run with an LLM router selecting… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/agent-trajectories-swesmith-random-subset.SWE-smith-trajectories
SWE-smith Trajectories
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Paper
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This dataset contains the 5017 trajectories we fine-tuned Qwen 2.5 Coder Instruct on, leading to
SWE-agent-LM-32B, a coding LM agent that
achieve 40.2% on SWE-bench Verified (no verifiers or multiple rollouts, just 1 attempt per instance).
Trajectories were generated by running SWE-agent + Claude 3.7 Sonnet on task instances from
the SWE-smith dataset.
swe-smith-py-glm5.2-tiny
swe-smith-py-glm5.2-tiny
SWE-smith Python trajectories collected with GLM-5.2, filtered to evaluation-resolved instances, and exported as TRL-style chat messages for supervised fine-tuning (tool calling + <think> reasoning).
Intended publish path: pyromind/swe-smith-py-glm5.2-tiny.
Summary
Item
Value
Model
openai/glm-5.2-fp8
Agent
mini-swe-agent (2.4.5)
Environment
Docker (/testbed)
Repo / task family
oauthlib SWE-smith mutations
Eval run… See the full description on the dataset page: https://huggingface.co/datasets/pyromind/swe-smith-py-glm5.2-tiny.