Why are there so many Python packaging tools?

The proliferation of Python package-building tools (e.g., setuptools, pip, flit, hatch) reflects Python’s historical development, evolving needs of different user communities, and ongoing efforts to solve complex packaging challenges.

Python’s packaging ecosystem evolved organically over decades. The language itself existed for years before the release of the first package manager, distutils, with Python 1.6 in 2000. Because of the limitations of distutils, community members developed the setuptools library in 2004. Despite not being part of the standard library, setuptools became the defacto packaging tool over the next ten years.

Packaging requirements became increasingly complex as Python grew in popularity across scientific computing, web development, data science, and enterprise software. Scientists needed reproducible environments with compiled extensions. Web developers needed dependency locking for deployments. Enterprise users needed private package repositories and security features. The original tools struggled to meet these diverse needs.

The limitations of setuptools became increasingly apparent. It lacked crucial features like build-time dependency declaration and version management. While various solutions emerged to address these gaps, they were often fragile and difficult to maintain. More problematically, the deep integration of setuptools into the Python ecosystem made it nearly impossible to use alternative approaches.

PEP 517 addressed this lock-in by defining a standard interface for Python build systems in 2017. This standardization enabled a new generation of packaging tools including flit, hatch, and PDM to emerge, each implementing the standard differently to serve specific user needs. Modern tools like UV build upon these standards while focusing on performance and improved user experience.

Rather than viewing this diversity of tools as problematic, it represents the ecosystem’s healthy response to Python’s growth across different domains. Each tool makes different tradeoffs to serve its target users effectively, whether prioritizing simplicity, performance, reproducibility, or enterprise features.