Hatch

Hatch is a Python project manager for developers who want a shared tool for shaping project workflows. It is best suited to Python developers who value repeatable builds and configurable environments; its breadth is useful, though it is not a hosted development workspace.

Overview

Hatch spans project creation and maintenance: start from a template, initialize an existing project, or migrate setuptools configuration from setup.py. It also supports lockfiles, dependency groups, and private registries, making it a practical fit for projects that need more than a basic package installer.

Its scope is deliberately project-centered. Hatch helps organize environments, checks, builds, and releases, but readers looking for a hosted workspace should choose a different kind of product.

Key features

Environments, Python, and builds

Hatch manages environments, supports custom scripts and UV, and can use manually installed Python or install Python automatically as part of an environment. Reproducible builds are a strong reason to consider it when a team wants more consistent project outputs. The options offer flexibility, but teams should be prepared to choose and maintain the workflow that suits their projects.

Testing, analysis, and publishing

The test command runs pytest with selected plugins and coverage.py by default, while static analysis is backed by Ruff. Hatch can publish packages to PyPI and other package indices. Together, these functions make it useful as a project workflow hub, rather than merely a way to install dependencies.

Setup and extensions

Template-based creation and initialization of existing projects lower the friction of adopting Hatch, including for projects migrating from setuptools configuration in setup.py. Its pluggy-based extension system covers environments, builders, publishers, and other project tasks. That reach can accommodate specialized workflows, though relying on extensions means making choices beyond Hatch's built-in functionality.

Visual Studio Code can discover Hatch environments through the vscode-python extension. Installation options include standalone binaries or installers for Windows, macOS, and Linux, alongside pip, pipx, Homebrew, Conda, and other methods. An official GitHub Actions install action is also documented.

Pricing

Hatch costs nothing and is MIT-licensed open-source software. There is no free trial because the product is free; no paid plan or usage cap is stated. That makes it a straightforward choice for individual developers and teams seeking a no-cost project manager, without a lower tier that omits features.

Platforms

Hatch supports Linux, macOS, and Windows. Its cross-platform availability and installation choices suit developers working across those operating systems, while Visual Studio Code environment discovery offers an integration for users of that editor.

Who it's for

Hatch is a strong fit for Python developers who want project setup, environments, testing, static analysis, reproducible builds, and package publishing under one project manager. It is also appealing to teams that may need plugins to adapt project tasks. Developers who only need a package installer may prefer a narrower tool; those seeking a graphical hosted workspace should look elsewhere.

Pros and cons

• Pros: A broad project workflow covers templates, environments, tests, analysis, builds, and publishing, reducing the need to assemble those tasks around a basic installer.
• Pros: Reproducible builds, lockfiles, dependency groups, and private registry support serve projects with more structured dependency and release workflows.
• Pros: Free, MIT-licensed use and support for Linux, macOS, and Windows keep the tool accessible across common developer platforms.
• Cons: Its range of workflow features and extension points brings configuration choices that may be unnecessary for developers who only need package installation.
• Cons: Hatch is a project manager, not a hosted workspace, so it is not the right choice for readers seeking an online development environment.

Alternatives

Choose pip when a basic free package installer is enough and Hatch's broader project workflow would be unnecessary. pip-tools is another free option for command-line tools installed per virtual environment, with Python 3.9 or later required.

uv is a free, open-source Python package alternative for readers who prefer that tool. conda-lock is a free, MIT-licensed option for readers looking specifically for a lockfile tool.

For package hosting alongside an open-source package workflow, Pixi offers free prefix.dev hosting with a global CDN, 100GB of public packages, and unlimited channel users. ActiveState Platform may suit readers who want a freemium alternative with a public-projects-only free organization plan and a free trial; its paid organization plan has custom pricing.

Astral is a free, permissively licensed collection of open-source tools including Ruff, uv, and ty, for readers who want that toolset. Miniconda is another free option, though package access through Anaconda repositories is subject to its Terms of Service.

Browse more options in Python Package Managers.

Verdict

Choose Hatch if you want a free, cross-platform Python project manager that brings environments, tests, builds, and publishing into one configurable workflow. Its strongest case is breadth with reproducible builds and extension points; look elsewhere if you need only a basic installer or want a hosted workspace.