featurebench 0.1.0

FeatureBench Pipeline - A test-driven data generation pipeline for building and evaluating feature-level coding benchmarks

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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

🎁 2026.02.06: 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

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 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

Run inference:

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

Run evaluation:

fb eval \
    -p runs/<timestamp>/output.jsonl \
    --split lite

🧭 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:

@misc{zhou2026featurebenchbenchmarkingagenticcoding,
      title={FeatureBench: Benchmarking Agentic Coding for Complex Feature Development}, 
      author={Qixing Zhou and Jiacheng Zhang and Haiyang Wang and Rui Hao and Jiahe Wang and Minghao Han and Yuxue Yang and Shuzhe Wu and Feiyang Pan and Lue Fan and Dandan Tu and Zhaoxiang Zhang},
      year={2026},
      eprint={2602.10975},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2602.10975}, 
}

📧 Contact

If you have any questions, feel free to contact [e-mail] or [e-mail].