conda-lock vs Hatch

conda-lock is a lightweight library for producing reproducible lockfiles for conda environments. It solves dependencies separately for each target platform, allowing installations from generated lockfiles to avoid running the conda solver. Inputs can come from environment.yml, conda-build meta.yaml, or pyproject.toml dependency specifications. Its default unified lockfile covers multiple platforms and can be rendered into other lockfile formats. Documented target examples include Linux, macOS, Windows, and additional Linux architectures. The project describes use alongside conda, mamba, pip, Docker, and pixi, and provides installation routes through pip, conda, and mamba; pipx or condax are recommended for isolated installation. Mixed conda-and-pip locking is experimental. Channel access supports token or basic authentication, with environment-variable substitution for credentials. The unified lockfile can omit raw credential-bearing channel URLs when it matches credential portions to environment variables, although rendered explicit lockfiles may contain substituted variables. Private pip repository support is limited to legacy PyPI repositories using basic authentication. The software is MIT-licensed and free.

Hatch is a free, MIT-licensed Python project manager for creating projects and managing development environments on Linux, macOS, and Windows. It can start projects from templates or initialize existing ones, including migrating setuptools configuration from setup.py. Environment management supports custom scripts and UV, with manual Python installations or automatic installation as part of an environment. Hatch also supports reproducible builds, test execution, and static analysis backed by Ruff. For publishing, it can upload packages to PyPI and other package indices; lockfiles, dependency groups, and private registries are listed capabilities. Its plugin system uses pluggy, with documented extension types for environments, builders, publishers, and other project tasks. Visual Studio Code can discover Hatch environments through the vscode-python extension, and an official GitHub Actions install action is documented. Installation options include standalone installers or binaries, pip, pipx, Homebrew, Conda, and other methods. The documentation provides a self-report command that prepares a GitHub issue for unexpected behavior.