SWE Lab

Tooling to build, run, enrich, audit and fix SWE (a.k.a. coding agent) evaluation data.

Tasks

  • Related-files annotation (src/swe_lab/pipelines/related_files/) — for each task instance, produce a ground-truth list of the code snippets a model needs to read to solve it. Shipped: 100 instances annotated & QA'd. See pipelines/related_files/README.md.

  • Quality auditing (planned) — flag "skewed" eval examples that no longer measure real capability (ambiguous specs vs. overly-specific tests, broken environments, contamination, brittle graders), in the spirit of OpenAI's Separating signal from noise in coding evaluations. Not started; it will land as a sibling under pipelines/.

The overall roadmap and design live in docs/README.md.

Setup

Prerequisites

  • uv for environment and dependency management
  • direnv for auto-activating the environment
  • Python 3.13 (uv will install it automatically if missing)

1. Clone

git clone https://github.com/Luolc/swe-lab.git
cd swe-lab

Optional — the --capture proxy mode compiles the standalone cc-reverse-proxy Go project. It is not a submodule: by default it is looked up as a sibling checkout next to this repo (../cc-reverse-proxy/reverse_proxy.go); clone it there, or point CC_REVERSE_PROXY_SRC at its reverse_proxy.go. The default stream capture needs none of this.

2. Set up the environment

uv sync          # create .venv and install all (incl. dev) dependencies
direnv allow     # auto-activate the venv on cd (uses .envrc)

If you don't use direnv, activate manually with source .venv/bin/activate.

Install the pre-commit hooks (ruff, pyink, isort, basedpyright, uv-lock):

uv run pre-commit install

3. Download the datasets

Dataset data files are gitignored and must be downloaded locally. See datasets/README.md for the list of available datasets and per-dataset download instructions.

Disclaimer

This is a personal project and is not affiliated with any company. The content does not reflect any specific company's projects, products or internal work.

Project links

Data verified by PyPI on Aug 7, 2026
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  • Released:
Newer release available (0.3.5)

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Avatar for Luolc from gravatar.com Luolc

Credits

Author: Liangchen Luo

GitHub Statistics

Data verified by PyPI on Aug 7, 2026
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License expression

Apache-2.0
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Requires

Python <3.14, >=3.13

Tags

agent coding-agent dataset evaluation llm sandbox swe-bench swe-bench-pro

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