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 Jul 31, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.5)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for Luolc from gravatar.com Luolc

Credits

Author: Liangchen Luo

GitHub Statistics

Data verified by PyPI on Jul 31, 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
View SPDX License List

Requires

Python <3.14, >=3.13

Tags

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

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

swe_lab-0.1.6.tar.gz (116.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

swe_lab-0.1.6-py3-none-any.whl (157.6 kB view details)

Uploaded Python 3

File details

Details for the file swe_lab-0.1.6.tar.gz.

File metadata

  • Download URL: swe_lab-0.1.6.tar.gz
  • Upload date:
  • Size: 116.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for swe_lab-0.1.6.tar.gz
Algorithm Hash digest
SHA256 b14da50a1716da0fb4325dd86791a5efecabd773b45dfccaeb033494b85787b6
MD5 a8e7fde196fd6a90bb2a6c7968a7b27d
BLAKE2b-256 a51edacf215ef639b27f4a9ea207a8394e094cd6872b34532d8533451f3b1118

See more details on using hashes here.

Provenance

The following attestation bundles were made for swe_lab-0.1.6.tar.gz:

Publisher: publish.yml on Luolc/swe-lab

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file swe_lab-0.1.6-py3-none-any.whl.

File metadata

  • Download URL: swe_lab-0.1.6-py3-none-any.whl
  • Upload date:
  • Size: 157.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for swe_lab-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 da3eaf2d3120d6c2d761027690f95137e8d99ee5448eb941c5fef41a2d1aaa5b
MD5 f76adf277815a4a0aac5d7a249a001df
BLAKE2b-256 aa687df9591b8d502b7b0b854cc8d358c09c3708b63e21022de63b29a1a775f9

See more details on using hashes here.

Provenance

The following attestation bundles were made for swe_lab-0.1.6-py3-none-any.whl:

Publisher: publish.yml on Luolc/swe-lab

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.3.5

2 files

0.3.4

2 files

0.3.3

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.14

2 files

0.2.13

2 files

0.2.12

2 files

0.2.11

2 files

0.2.10

2 files

0.2.9

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.12

2 files

0.1.11

2 files

0.1.10

2 files

0.1.9

2 files

0.1.8

2 files

0.1.7

2 files

This release

0.1.6 This release

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

0.1.2

2 files

0.1.1

2 files

0.1.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page