SWE Lab

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

What's in it

  • The run engine (src/swe_lab/sandbox/, workflow/) — one sandboxed run of a coding agent over a benchmark instance, composed from three plug-in axes: a harness (claude_code, codex, grok_build), a dataset (SWE-Bench Pro, DeepSWE 1.1) and an eval method (unit-test grading). A workflow chains those steps — solve, grade, or both — and is run by name:

    python -m swe_lab run --list                                  # what can be run
python -m swe_lab run rollout_and_unit_test <instance_id>     # solve, then grade

  • Benchmark integrity (src/swe_lab/git/, integrity/) — the task repo's future is stripped out of the container before the agent starts (and the strip is proved), and each run is swept for the ways an agent can reach the answer anyway. Detection, never a gate — ADR-0010.

  • Related-files annotation (src/swe_lab/pipelines/related_files/) — for each task instance, a ground-truth list of the code snippets a model needs to read to solve it. Shipped: 731/731 SWE-Bench Pro instances annotated & QA'd.

  • 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; the nearest shipped work is the benchmark-integrity detection above.

The overall roadmap and status live in docs/README.md; the codebase map, commands and hazards in docs/conventions.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 (the full set is listed in docs/conventions.md):

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 Sep 7, 2026
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  • Released:
Newer release available (0.3.7)

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Avatar for Luolc from gravatar.com Luolc

Credits

Author: Liangchen Luo

GitHub Statistics

Data verified by PyPI on Sep 7, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

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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