UV: The Ultra-Fast Python Package Manager and Resolver

Python package management has long been a source of frustration for many developers. From slow resolution times to dependency conflicts, the limitations of traditional tools like pip have spawned numerous alternatives, each with their own tradeoffs. Enter uv – a revolutionary package manager that promises ultra-fast performance while maintaining compatibility with the Python ecosystem you already know.

In this guide, we’ll explore uv’s key features, installation methods, and how to integrate it into your existing Python workflows for dramatically improved performance.

What is uv?

uv is an extremely fast Python package installer and resolver, written in Rust. It serves as a replacement for pip, pip-tools, and virtualenv, focusing on two primary goals:

1. Speed: uv performs operations dramatically faster than traditional Python package managers
2. Compatibility: uv maintains compatibility with existing Python tooling and standards

Created by the team at Astral, uv is designed to solve common frustrations with Python packaging while preserving familiar workflows.

Key Features

• Ultra-fast package installation and resolution
• Virtual environment management
• Python version management
• Complete compatibility with requirements.txt and pyproject.toml files
• No new manifest formats to learn
• Direct replacement for common pip commands

Installing uv

Linux/macOS Installation

The simplest way to install uv on Linux or macOS is via the installation script:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows Installation

For Windows users, install with PowerShell:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Updating uv

To update uv to the latest version:

uv self update

Advantages of uv

Blazing-Fast Performance

uv’s most notable advantage is its speed. Benchmarks show that uv can install packages up to 10-100x faster than pip, especially for larger projects with complex dependency trees. This performance boost comes from:

• Rust implementation with optimized concurrency
• Efficient dependency resolution algorithm
• Smart caching of package metadata and wheels

Storage-Efficient Global Cache

One of uv’s standout features is its intelligent global cache, which significantly reduces disk space usage across multiple projects:

# View cache location and statistics
uv cache info

# Clean the cache to free up space
uv cache clean

Unlike traditional package managers that might duplicate package installations across environments, uv stores downloaded wheels in a single global cache. This approach:

• Dramatically reduces disk usage when working with multiple projects
• Eliminates redundant downloads of the same package versions
• Persists across project deletions, making new environment creation faster
• Automatically manages cache size with configurable retention policies

The cache is particularly valuable for CI/CD environments and development machines with many Python projects.

Simplified Environment Management

uv integrates virtual environment management directly into the CLI:

# Create and activate a new environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install packages into the active environment
uv pip install numpy pandas

Seamless Python Version Management

uv can manage multiple Python versions through its uv python command:

# List available Python versions
uv python list

# Install a specific Python version
uv python install 3.11

Working with Existing Requirements Files

uv maintains complete compatibility with requirements.txt files:

# Install from requirements.txt
uv pip install -r requirements.txt

# Generate requirements.txt with exact versions
uv pip freeze > requirements.txt

Comparison with Existing Package Managers

While each tool has its strengths, uv excels at providing maximum performance while requiring minimal changes to existing workflows.

Using uv

Basic Package Management

uv provides a familiar interface for package management:

# Install a package
uv pip install requests

# Install multiple packages
uv pip install numpy pandas matplotlib

# Install a specific version
uv pip install "django==4.2.0"

# Uninstall a package
uv pip uninstall requests

Managing Virtual Environments

Creating and working with virtual environments is straightforward:

# Create a new environment
uv venv

# Create with a specific Python version
uv venv --python=3.10

# Create with a custom path
uv venv ./custom-env

# Activate the environment (not handled by uv)
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Working with Requirements Files

uv is fully compatible with requirements.txt workflows:

# Install from requirements.txt
uv pip install -r requirements.txt

# Generate requirements.txt with pinned versions
uv pip freeze > requirements.txt

# Upgrade all packages in requirements.txt
uv pip install -r requirements.txt --upgrade

Advanced Usage

Integration with pyproject.toml

uv works seamlessly with PEP 621 compliant pyproject.toml files:

# Install a project and its dependencies
uv pip install -e .

# Install development dependencies
uv pip install -e ".[dev]"

Using with Existing Virtual Environments

uv can work within any existing virtual environment:

# Activate your existing environment first
source my-existing-env/bin/activate

# Then use uv normally
uv pip install requests

Script Tools and Utilities

uv includes several useful utilities:

# Run uv run by itself to see available commands and python versions
uv run

# Run Python with optimized settings
uv run script.py

# Execute a module
uv run python3.14 -m pytest

Migrating from pip

Transitioning to uv from pip is straightforward:

1. Replace pip commands: Most pip commands work with uv by simply changing pip to uv pip
2. Keep existing files: No need to modify requirements.txt or pyproject.toml files
3. Maintain existing environments: uv works with your current virtual environments

Here’s a quick migration cheatsheet:

Best Practices

• Use lockfiles: For reproducible builds, use uv pip compile to generate lockfiles
• Leverage uv’s speed: uv’s performance makes it practical to regenerate lockfiles more frequently
• Add uv to CI/CD pipelines: Improve build times by replacing pip with uv in automated workflows
• Keep virtual environments local: Store environments in your project directory for clarity
• Use “–system” flag for containers: In Docker containers, use uv pip install --system to install globally

Common Issues and Solutions

Package Resolution Conflicts

Problem: Dependency conflicts between packages Solution: Use uv pip install --resolution=highest to try alternative resolution strategies

Environment Activation Issues

Problem: Environment not being activated properly Solution: Remember that uv creates environments but doesn’t activate them – use source or the appropriate activation script

Missing Wheels for Some Platforms

Problem: Installation fails due to missing wheels Solution: Use uv pip install --use-build-system to build from source when wheels aren’t available

Conclusion

uv represents a significant advancement in Python package management, offering exceptional performance while maintaining compatibility with existing workflows and tools. By replacing pip with uv in your projects, you can dramatically speed up installation times and dependency resolution without learning new manifest formats or changing your development process.

As the Python ecosystem continues to evolve, tools like uv demonstrate how technical innovation can solve long-standing pain points while respecting established conventions.

Additional Resources

• Official uv Documentation
• Python Packaging User Guide
• PEP 621: Storing project metadata in pyproject.toml