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FeatureBench is a test-driven data generation and evaluation pipeline for feature-level coding benchmarks. It provides a unified CLI to run inference, evaluation, and dataset generation.

📰 News

🚀 2026.03.27: We released the fast split containing 100 instances (a subset of full split). These instances require no GPU and are optimized for rapid evaluation. On an Intel Xeon Platinum 8457C with 944GB RAM, the average evaluation time per instance using gold patches is 57.2 seconds.

🎁 2026.02.06: We now support one-click inference for mainstream agent frameworks, including OpenHands, Claude Code, Codex, Gemini CLI, and mini-swe-agent. All supported agent frameworks can be found here. We have also open-sourced the FeatureBench data pipeline.

🚀 Quickstart

Prerequisites:

• uv for Python environment management
• docker for reproducible builds and evaluation

# pypi
pip install featurebench
# or uv add featurebench

# local
git clone https://github.com/LiberCoders/FeatureBench.git
cd FeatureBench
uv sync
source .venv/bin/activate

Configure:

cp config_example.toml config.toml

See docs/config.md for a comprehensive reference (harness, infer, data pipeline) with examples.

Optional: pre-pull images to reduce network variance:

fb pull --mode lite                 # lite split image list (13 images)
fb pull --mode fast                 # fast split image list (18 images)
fb pull --mode full                 # full split image list (24 images)
fb pull --mode /path/to/images.txt  # one image name per line

# full list: featurebench/resources/constants/full_images.txt
# lite list: featurebench/resources/constants/lite_images.txt
# fast list: featurebench/resources/constants/fast_images.txt

Run inference:

fb infer \
    --config-path config.toml \
    --agent mini_swe_agent \
    --model openai/qwen3-coder-480b-a35b-instruct \
    --split fast

Run evaluation:

fb eval \
    -p runs/<timestamp>/output.jsonl \
    --split fast
    # use -p gold to verify the gold patches

🧭 CLI Overview

fb provides three core commands:

• fb infer runs featurebench.infer.run_infer (docs: docs/infer_cli_arg.md)
• fb eval runs featurebench.harness.run_evaluation (docs: docs/harness_cli_arg.md)
• fb data runs featurebench.pipeline (docs: docs/pipeline.md)

✍️ Citation

If you found FeatureBench useful, please cite us as:

@article{zhou2026featurebench,
  title={FeatureBench: Benchmarking Agentic Coding for Complex Feature Development},
  author={Zhou, Qixing and Zhang, Jiacheng and Wang, Haiyang and Hao, Rui and Wang, Jiahe and Han, Minghao and Yang, Yuxue and Wu, Shuzhe and Pan, Feiyang and Fan, Lue and others},
  journal={arXiv preprint arXiv:2602.10975},
  year={2026}
}

📧 Contact

If you have any questions, feel free to contact qixingzhou1125@gmail.com or zjcheng2022@gmail.com.