For most new Python packages, put project metadata in pyproject.toml, choose a build backend that fits the project, and use a frontend such as build to create the distributions. The frontend runs the build; the backend decides how the package is assembled. The right backend depends on your package layout, compatibility needs, and whether you build native extensions.
What Python build tools do
Building a Python package means producing files that users or package indexes can install and distribute. The two main artifacts are a source distribution (sdist), which contains source files and packaging metadata, and a wheel, which is a built distribution. A wheel may be pure Python or specific to a platform and Python implementation, as with many compiled extensions.
The backend controls package-specific work: discovering files, deciding what goes into the artifacts, generating metadata, and creating the distributions. Because inclusion rules and metadata can differ, inspect the resulting artifacts rather than assuming that a successful build contains exactly what you intended. The PyPA packaging tutorial shows a common starter layout with a license, pyproject.toml, README, src/ package, and tests.
Frontend vs. backend: what is the difference?
A build frontend reads project configuration and invokes standardized build hooks. A backend implements those hooks and performs the actual package build. For example, build is a frontend commonly run as python -m build; it can work with different backends. This separation lets the same frontend build packages configured for different backend systems.
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With an isolated build, the frontend can install the requirements listed for the build in a temporary environment before invoking the backend. This helps separate build-time dependencies from the project environment. The build frontend documentation explains that process, and its backend guide describes the division of responsibility.
Which Python build backend should you use?
Choose based on the package and the workflow you need, not on an unsupported assumption that one backend is universally fastest or most popular. The distinctions below reflect documented use cases, not a comparative performance test. Check each project’s current documentation before migrating or relying on a particular feature.
| Project or workflow | Candidate backend | Why it may fit |
|---|---|---|
| Straightforward pure-Python package | Flit-core or Hatchling | Both are options for relatively simple packages. Hatchling also offers plugins and common layout conventions. |
| Broad compatibility or substantial customization, including C extensions, namespace packages, or entry points | Setuptools | It is mature and capable, while carrying more legacy concepts and configuration complexity. |
| C or C++ extension built with CMake | scikit-build-core | It integrates package building with CMake and modern package metadata. |
| Extension project already using Meson | meson-python | It integrates the package build with Meson. |
| Existing Poetry-centered workflow | poetry-core / Poetry | It can keep packaging aligned with that ecosystem. Custom [tool.poetry] metadata may be less interoperable in some contexts. |
| PDM workflow or a need for dynamic metadata or build hooks | pdm-backend | It supports standard metadata along with backend-specific features. |
These choices are not mutually exclusive recommendations for every project. In particular, native extensions make the build system and compiler workflow important: select a backend that fits the native build system already used by the project, or plan the migration deliberately.
What belongs in pyproject.toml?
pyproject.toml is the central modern configuration file, but its tables have different jobs. The PyPA guide to writing pyproject.toml recommends [project] metadata for new projects and explains backend declarations. The formal specification defines the file’s standard fields.
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[build-system]declares the packages needed to build and the backend import path.[project]holds standard project metadata understood by most backends, such as the name, version, and dependencies.[tool]contains tool-specific configuration, including backend-specific settings when necessary.
Backend declarations should follow that backend’s documentation. The current PyPA guide provides examples for Hatchling, setuptools, Flit, PDM, and uv-build. Their import paths include hatchling.build, setuptools.build_meta, flit_core.buildapi, pdm.backend, and uv_build. Treat version examples in a guide as time-sensitive: check the backend documentation for the version you intend to install.
License metadata and backend versions
The current PyPA guide lists these minimum backend versions for PEP 639 license metadata support: Hatchling 1.27.0, setuptools 77.0.3, flit-core 3.12, pdm-backend 2.4.0, poetry-core 2.2.0, and uv-build 0.7.19. These are version-specific support thresholds, not a promise that older releases support the same fields. The specification defines license as an SPDX license expression and license-files as paths or glob patterns for legal notices included in distribution archives.
Do you still need setup.py?
Not necessarily. For a new project, the PyPA guidance recommends standard metadata in [project] in pyproject.toml. Setuptools still supports legacy setup.py and setup.cfg configuration; those formats remain valid for compatibility and special cases. You do not need to replace a working legacy project solely because the modern configuration format exists, but new work can generally start with pyproject.toml.
Poetry’s metadata format is also version-sensitive: before Poetry 2.0, released January 5, 2025, it supported only [tool.poetry] metadata; from version 2.0 onward it supports [project] as well. Check the documentation for the installed Poetry version if you are changing metadata layout.
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How to build a wheel and source distribution
The following is a minimal example for a pure-Python project using Hatchling. It assumes a recent Python installation, a project directory with a package under src/, and a README. The example version is a configuration choice, not a universal minimum; consult the backend’s documentation and use compatible versions for your environment.
1. Declare the backend and project metadata
Save a configuration like this as pyproject.toml, adjusting the package name, version, description, and README to your project:
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "example-package"
version = "0.1.0"
description = "An example Python package"
readme = "README.md"
requires-python = ">=3.9"
[tool.hatch.build.targets.wheel]
packages = ["src/example_package"]Use your actual import package directory in the wheel target. The fields shown are an example; a real project should declare its applicable metadata and dependencies. If the backend needs a particular version or additional build requirements, follow its documentation and specify those requirements in [build-system].requires.
2. Build with the frontend
From the project root, install the frontend and ask it to build the distributions:
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python -m pip install build
python -m buildBy default, this builds an sdist and wheel into dist/. The frontend uses the backend declared in pyproject.toml and, by default, builds in an isolated environment using the declared build requirements.
3. Inspect what you are about to publish
Check that the wheel and sdist both exist and contain the intended package files, README, license notices, and metadata. For example, list archive contents with:
python -m zipfile -l dist/example_package-0.1.0-py3-none-any.whl
python -m tarfile -l dist/example_package-0.1.0.tar.gzArtifact names vary with project version and wheel tags, so replace the example filenames with the files actually present in dist/. If files are missing or extra files appear, check the backend’s file-selection rules and its configuration before publishing. Backend behavior determines artifact contents.
Common build problems and fixes
- Missing build backend or module: The declared backend import path or build requirement may be incorrect, or the required package may not have been installed. Verify
build-backendandrequiresagainst that backend’s documentation, then rebuild. - Build requirement cannot be installed: The isolated environment must be able to resolve the packages named under
[build-system].requires. Check spelling, version constraints, and access to the configured package index; avoid relying on a locally installed backend that is not declared. - Wheel is missing package files: Review the backend’s package discovery and inclusion settings. With a
src/layout, ensure the configured package path matches the actual directory, then inspect the rebuilt wheel. - License or metadata is rejected or absent: Confirm the metadata is expressed using the supported fields and syntax, and that the backend version supports those fields. For PEP 639 license metadata, compare your backend version with the version-specific thresholds listed above.
- Native extension fails to compile: Confirm the project has the compiler and native build prerequisites required by its chosen toolchain. Match the backend to the project’s CMake, Meson, or other existing build workflow, and consult that backend’s documentation for platform-specific setup.
- Legacy setup configuration is ignored or conflicts with modern metadata: Identify which source of configuration the backend is using. Move standard metadata to
[project]when appropriate, and retain legacy files only where needed for compatibility or documented special behavior.
Build reliability, performance, and cost
Isolated builds make declared build requirements explicit and reduce dependence on incidental packages installed in a developer’s environment. They can require fetching the declared build dependencies, so a build may fail when those dependencies cannot be resolved. Reproducibility still depends on the project’s requirements, tool versions, and native toolchain; the configuration alone does not guarantee identical artifacts on every machine.
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No measured performance comparison among the backends is established here, so choose by supported capabilities and project fit rather than assumed speed. Review the produced artifacts before release, especially after changing backends or build configuration. The referenced packaging documentation does not establish a universal build cost; practical cost depends on the project’s dependencies, native compilation, and build environment.
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