README.md

# SWE-Next: Scalable Real-World Software Engineering Tasks for Agents

<p align="left">
  <a href="https://arxiv.org/abs/2603.20691"><img alt="Paper" src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white"></a>
  <a href="https://tiger-ai-lab.github.io/SWE-Next/"><img alt="Project Page" src="https://img.shields.io/badge/Project%20Page-Website-4285F4?style=for-the-badge&logo=googlechrome&logoColor=white"></a>
  <a href="https://github.com/TIGER-AI-Lab/SWE-Next"><img alt="Code" src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white"></a>
  <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next-SFT-Trajectories"><img alt="SFT Trajs" src="https://img.shields.io/badge/SFT%20Trajs-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
  <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next"><img alt="Dataset" src="https://img.shields.io/badge/Dataset-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
  <a href="https://huggingface.co/TIGER-Lab/SWE-Next-7B"><img alt="Model 7B" src="https://img.shields.io/badge/Model%207B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
  <a href="https://huggingface.co/TIGER-Lab/SWE-Next-14B"><img alt="Model 14B" src="https://img.shields.io/badge/Model%2014B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
</p>

## 📰 News

- **2026-04-07**: SWE-Next is now publicly released!

## 📖 Introduction

**SWE-Next** introduces reusable **repo-quarter profiles**, which reuse the same environment across nearby commits in time while keeping each task run separate and reproducible. Using only **30 hours** and **639GB** of environment storage, SWE-Next processes **3,971** seed repositories and **102,582** candidate commit pairs mined from real merged PRs to construct a dataset of **2,308** self-verifying instances. SWE-Next improves downstream pass@1 on SWE-Bench Verified and SWE-Bench Lite with fewer or comparable training trajectories, making large-scale executable data collection far more practical and accessible for research.



## ✨ Highlights

- **Scaled Environment Generation** — SWE-Next is an execution-grounded framework that turns real merged-PR commits into self-verifying SWE tasks, and pairs them with high-signal trajectories.

- **Repo-quarter Profiles** - A reusable environment mechanism that amortizes build and storage cost across temporally nearby commits, substantially reducing resource requirements and accelerating large-scale executable SWE data collection.


## 🛠️ Setup

### Prerequisites

- Python 3.10+
- Docker (for environment execution)
- [uv](https://github.com/astral-sh/uv) package manager

### Installation

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

git clone https://github.com/TIGER-AI-Lab/SWE-Next.git
cd SWE-Next
uv venv && source .venv/bin/activate
uv sync && uv pip install -e .
```

## 🤗 Data & Models

Pre-built artifacts are available on HuggingFace. Download them into `data/` before running the pipeline:

| Artifact | Description | Download |
|----------|-------------|---------|
| `packages_python_filtered` | 3,900+ Python package list used as pipeline input | `huggingface-cli download TIGER-Lab/packages_python_filtered --repo-type dataset --local-dir data/packages_python_filtered` |
| `new_commit_better_repos` | Repos with confirmed NEW_COMMIT_BETTER commits | `huggingface-cli download TIGER-Lab/new_commit_better_repos --repo-type dataset --local-dir data/new_commit_better_repos` |
| `SWE-Next` | Final curated dataset (2,308 instances) | `huggingface-cli download TIGER-Lab/SWE-Next --repo-type dataset --local-dir data/SWE-Next` |
| `SWE-Next-SFT-Trajectories` | SFT training trajectories | `huggingface-cli download TIGER-Lab/SWE-Next-SFT-Trajectories --repo-type dataset --local-dir data/SWE-Next-SFT-Trajectories` |

Pre-trained models:

| Model | Download |
|-------|---------|
| SWE-Next-7B | `huggingface-cli download TIGER-Lab/SWE-Next-7B --repo-type model --local-dir LlamaFactory/saves/SWE_Next_7B` |
| SWE-Next-14B | `huggingface-cli download TIGER-Lab/SWE-Next-14B --repo-type model --local-dir LlamaFactory/saves/SWE_Next_14B` |

## 🐳 Environment Generation

SWE-Next extends environment generation to 3,900+ Python packages.

The supported package list is maintained in [`data/packages_python_filtered/packages_python_filtered.csv`](data/packages_python_filtered/packages_python_filtered.csv) and target repositories in [`data/new_commit_better_repos/new_commit_better_repos.csv`](data/new_commit_better_repos/new_commit_better_repos.csv).

## 🚀 Data Pipeline (One-Click)

`run_pr_pipeline.zsh` automates the full data collection pipeline. It reads `data/packages_python_filtered/packages_python_filtered.csv`, clones the repos automatically, and processes them end-to-end. If the CSV is not present it falls back to repos already cloned under `outputs/upstream_repos/`.

**Prerequisites:** copy `.env.template` to `.env` and fill in your credentials:
```
OPENAI_API_KEY=...        # required for synthetic issue generation
GITHUB_TOKEN=...          # required for fetching PRs
DOCKERHUB_USERNAME=...    # required for pushing Docker images
DOCKERHUB_TOKEN=...
DOCKERHUB_NAMESPACE=...   # your Docker Hub namespace
```

**Option 1 — Dataset only** (runs until `outputs/all_new_commit_better_pr.jsonl` is produced, no trajectories):
```bash
PR_GEN_TRAJ=0 zsh run_pr_pipeline.zsh
```

**Option 2 — Dataset + trajectories** (continues to run GPT-5-mini on the collected instances):
```bash
PR_GEN_TRAJ=1 PR_TRAJ_LLM_NAME=gpt-5-mini zsh run_pr_pipeline.zsh
```

To process a specific repo only:
```bash
PR_GEN_TRAJ=0 zsh run_pr_pipeline.zsh owner/repo
```

## 🏋️ Training

### Step 1 — Generate SFT Trajectories

Download the SWE-Next dataset first (see [Data & Models](#data--models)), then collect trajectories using a frontier LLM:

```bash
python src/swenext/agenthub/run/edit.py runagent_multiple \
  --dataset "data/SWE-Next/SWE_Next_dataset.jsonl" \
  --traj_dir "./traj/swe_next_sft" \
  --max_workers 8 \
  --k -1 \
  --llm_name "gpt-5-mini" \
  --use_fn_calling True \
  --temperature 0.2 \
  --max_steps 40 \
  --backend "docker"
```

Or skip this step and use the pre-collected trajectories from HuggingFace (download `SWE-Next-SFT-Trajectories` above).

### Step 2 — SFT Training

Clone [LlamaFactory](https://github.com/hiyouga/LLaMA-Factory) into the project root first:

```bash
git clone https://github.com/hiyouga/LLaMA-Factory.git LlamaFactory
```

Install LlamaFactory dependencies, then train (run from the project root):

```bash
cd LlamaFactory && pip install -e ".[torch,metrics]" && cd ..

# Train 7B agent
llamafactory-cli train train/swe_next_7B.yaml

# Train 14B agent
llamafactory-cli train train/swe_next_14B.yaml
```

Trained model checkpoints will be saved to `LlamaFactory/saves/SWE_Next_7B` and `LlamaFactory/saves/SWE_Next_14B`.

### Step 3 — Evaluate on SWE-Bench Verified

Start a vLLM server with the trained model, then run evaluation:

```bash
# Start vLLM server (in a separate terminal)
vllm serve LlamaFactory/saves/SWE_Next_7B \
  --served-model-name SWE-Next-7B \
  --port 8000

# Run evaluation on SWE-Bench Verified (8 parallel workers)
export LLM_BASE_URL="http://127.0.0.1:8000/v1"

python src/swenext/agenthub/run/edit.py runagent_multiple \
  --dataset "R2E-Gym/SWE-Bench-Verified" \
  --split "test" \
  --traj_dir "./traj/swe_bench_verified" \
  --max_workers 8 \
  --k -1 \
  --llm_name "openai/SWE-Next-7B" \
  --use_fn_calling False \
  --temperature 1 \
  --max_steps 40 \
  --backend "docker"
```

> Use the official [SWE-Bench evaluation harness](https://github.com/SWE-bench/SWE-bench) for final reported scores.

## 📝 Citation

```bibtex
@misc{liang2026swenextscalablerealworldsoftware,
      title={SWE-Next: Scalable Real-World Software Engineering Tasks for Agents}, 
      author={Jiarong Liang and Zhiheng Lyu and Zijie Liu and Xiangchao Chen and Ping Nie and Kai Zou and Wenhu Chen},
      year={2026},
      eprint={2603.20691},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2603.20691}, 
}
```