UV Package Manager

Comprehensive guide to using uv, an extremely fast Python package installer and resolver written in Rust, for modern Python project management and dependency workflows.

When to Use This Skill

• Setting up new Python projects quickly

• Managing Python dependencies faster than pip

• Creating and managing virtual environments

• Installing Python interpreters

• Resolving dependency conflicts efficiently

• Migrating from pip/pip-tools/poetry

• Speeding up CI/CD pipelines

• Managing monorepo Python projects

• Working with lockfiles for reproducible builds

• Optimizing Docker builds with Python dependencies

Core Concepts

1. What is uv?

• Ultra-fast package installer: 10-100x faster than pip

• Written in Rust: Leverages Rust's performance

• Drop-in pip replacement: Compatible with pip workflows

• Virtual environment manager: Create and manage venvs

• Python installer: Download and manage Python versions

• Resolver: Advanced dependency resolution

• Lockfile support: Reproducible installations

2. Key Features

• Blazing fast installation speeds

• Disk space efficient with global cache

• Compatible with pip, pip-tools, poetry

• Comprehensive dependency resolution

• Cross-platform support (Linux, macOS, Windows)

• No Python required for installation

• Built-in virtual environment support

3. UV vs Traditional Tools

• vs pip: 10-100x faster, better resolver

• vs pip-tools: Faster, simpler, better UX

• vs poetry: Faster, less opinionated, lighter

• vs conda: Faster, Python-focused

Installation

Quick Install

# macOS/Linux

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

# Windows (PowerShell)

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

# Using pip (if you already have Python)

pip install uv

# Using Homebrew (macOS)

brew install uv

# Using cargo (if you have Rust)

cargo install --git https://github.com/astral-sh/uv uv

Verify Installation

uv --version

# uv 0.x.x

Quick Start

Create a New Project

# Create new project with virtual environment

uv init my-project

cd my-project

# Or create in current directory

uv init .

# Initialize creates:

# - .python-version (Python version)

# - pyproject.toml (project config)

# - README.md

# - .gitignore

Install Dependencies

# Install packages (creates venv if needed)

uv add requests pandas

# Install dev dependencies

uv add --dev pytest black ruff

# Install from requirements.txt

uv pip install -r requirements.txt

# Install from pyproject.toml

uv sync

Virtual Environment Management

Pattern 1: Creating Virtual Environments

# Create virtual environment with uv

uv venv

# Create with specific Python version

uv venv --python 3.12

# Create with custom name

uv venv my-env

# Create with system site packages

uv venv --system-site-packages

# Specify location

uv venv /path/to/venv

Pattern 2: Activating Virtual Environments

# Linux/macOS

source .venv/bin/activate

# Windows (Command Prompt)

.venv\Scripts\activate.bat

# Windows (PowerShell)

.venv\Scripts\Activate.ps1

# Or use uv run (no activation needed)

uv run python script.py

uv run pytest

Pattern 3: Using uv run

# Run Python script (auto-activates venv)

uv run python app.py

# Run installed CLI tool

uv run black .

uv run pytest

# Run with specific Python version

uv run --python 3.11 python script.py

# Pass arguments

uv run python script.py --arg value

Package Management

Pattern 4: Adding Dependencies

# Add package (adds to pyproject.toml)

uv add requests

# Add with version constraint

uv add "django>=4.0,<5.0"

# Add multiple packages

uv add numpy pandas matplotlib

# Add dev dependency

uv add --dev pytest pytest-cov

# Add optional dependency group

uv add --optional docs sphinx

# Add from git

uv add git+https://github.com/user/repo.git

# Add from git with specific ref

uv add git+https://github.com/user/repo.git@v1.0.0

# Add from local path

uv add ./local-package

# Add editable local package

uv add -e ./local-package

Pattern 5: Removing Dependencies

# Remove package

uv remove requests

# Remove dev dependency

uv remove --dev pytest

# Remove multiple packages

uv remove numpy pandas matplotlib

Pattern 6: Upgrading Dependencies

# Upgrade specific package

uv add --upgrade requests

# Upgrade all packages

uv sync --upgrade

# Upgrade package to latest

uv add --upgrade requests

# Show what would be upgraded

uv tree --outdated

Pattern 7: Locking Dependencies

# Generate uv.lock file

uv lock

# Update lock file

uv lock --upgrade

# Lock without installing

uv lock --no-install

# Lock specific package

uv lock --upgrade-package requests

Python Version Management

Pattern 8: Installing Python Versions

# Install Python version

uv python install 3.12

# Install multiple versions

uv python install 3.11 3.12 3.13

# Install latest version

uv python install

# List installed versions

uv python list

# Find available versions

uv python list --all-versions

Pattern 9: Setting Python Version

# Set Python version for project

uv python pin 3.12

# This creates/updates .python-version file

# Use specific Python version for command

uv --python 3.11 run python script.py

# Create venv with specific version

uv venv --python 3.12

Project Configuration

Pattern 10: pyproject.toml with uv

[project]

name = "my-project"

version = "0.1.0"

description = "My awesome project"

readme = "README.md"

requires-python = ">=3.8"

dependencies = [

    "requests>=2.31.0",

    "pydantic>=2.0.0",

    "click>=8.1.0",

]

[project.optional-dependencies]

dev = [

    "pytest>=7.4.0",

    "pytest-cov>=4.1.0",

    "black>=23.0.0",

    "ruff>=0.1.0",

    "mypy>=1.5.0",

]

docs = [

    "sphinx>=7.0.0",

    "sphinx-rtd-theme>=1.3.0",

]

[build-system]

requires = ["hatchling"]

build-backend = "hatchling.build"

[tool.uv]

dev-dependencies = [

    # Additional dev dependencies managed by uv

]

[tool.uv.sources]

# Custom package sources

my-package = { git = "https://github.com/user/repo.git" }

Pattern 11: Using uv with Existing Projects

# Migrate from requirements.txt

uv add -r requirements.txt

# Migrate from poetry

# Already have pyproject.toml, just use:

uv sync

# Export to requirements.txt

uv pip freeze > requirements.txt

# Export with hashes

uv pip freeze --require-hashes > requirements.txt

For advanced workflows including Docker integration, lockfile management, performance optimization, tool comparison, common workflows, tool integration, troubleshooting, best practices, migration guides, and command reference, see references/advanced-patterns.md