Why Use uv Projects Instead of requirements.txt?

The Python ecosystem offers two main approaches to managing project dependencies: the traditional requirements.txt file and the newer pyproject.toml standard. Understanding the strengths and limitations of each helps inform better choices for Python development.

The Traditional Approach: requirements.txt

The requirements.txt file has been a cornerstone of Python development for many years. Its straightforward format makes it easy to read and modify by hand. Developers can quickly list their dependencies, and the file can even be hosted online and installed directly via URL (e.g., pip install -r https://example.com/requirements.txt). This simplicity, combined with universal support from tools like pip install and its faster alternative uv pip install, explains its enduring popularity.

The requirements.txt workflow

First, a developer creates a project directory and a virtual environment:

mkdir myproject
cd myproject
python -m venv .venv

Then, they must activate the virtual environment:

# On Unix or MacOS:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

After activation, they create a requirements.txt file listing dependencies:

requests>=2.28.0
pandas~=2.0.0

Finally, they install the dependencies into the activated environment:

pip install -r requirements.txt
# or
uv pip install -r requirements.tx

hen adding new dependencies, developers must:

1. Install the package with pip
2. Remember to add it to requirements.txt manually

requirements.txt limitations

Requirements files are fundamentally just lists of packages to install, lacking support for modern development workflows. When projects need different dependencies for development versus production, developers must maintain multiple files (like requirements-dev.txt).

Most critically, requirements.txt files don’t support the richer metadata needed for Python projects. For example, they can’t specify which version of Python is needed, making it possible to attempt installation in an incompatible environment. Similarly, they don’t natively support integration with Python packaging tools when bundling code for distribution.

The Modern Approach: pyproject.toml with uv

The Python ecosystem has evolved significantly since requirements.txt was introduced. The pyproject.toml file, defined by PEP 621, represents a modern standard for project configuration that addresses historical limitations. This standard is supported by a variety of tools including flit, hatch, pdm, poetry, and uv.

Starting a new project with uv requires just one command:

uv init myproject
cd myproject

This creates a project directory with a pyproject.toml file that serves as the project’s central configuration.

Adding dependencies becomes a single step that updates both the project configuration and environment:

uv add requests pandas

uv automatically creates and manages a virtual environment, removing the need to manually create or activate one.

When returning to the project, there’s no activation step required. The environment stays in sync with the project’s dependencies automatically. If another developer clones the project, they can get started with just:

uv sync

Using a modern tool like uv has a number of other benefits beyond just the simplied workflow.

Unified Project Configuration

Rather than spreading configuration across multiple files (requirements.txt, setup.py, setup.cfg, tox.ini, etc), pyproject.toml serves as a single source of truth for project metadata. It defines not just dependencies, but also the Python version required, project description, authors, and other essential metadata. This consolidation makes projects more maintainable and easier to understand.

Moreover, the same configuration works for both application projects and distributable packages.

Environment Isolation

While requirements.txt requires manual virtual environment creation and activation, uv projects handle this automatically. Developers can run their code with uv run and always get the correct, isolated environment without explicitly managing virtual environments. This automation reduces cognitive overhead while maintaining proper isolation practices.

Making the Choice

The Python packaging ecosystem continues to evolve, but pyproject.toml represents a significant step forward in project management. Its combination of standardization, rich metadata support, and tool integration makes it the recommended choice for modern Python development.