uv-expert

name	uv-expert
description	Expert guidance for uv Python package and project manager. Use when working with uv, Python dependency management, project setup, virtual environments, or when users mention uv commands, pip alternatives, or fast Python package management.

UV Expert

Expert guidance for uv, the extremely fast Python package and project manager written in Rust. UV provides a unified interface for Python dependency management, project creation, virtual environments, and more.

Additional Resources

For detailed API documentation and configuration options, see the UV Source Documentation which includes:

• Command Line Reference - Complete CLI command documentation
• Configuration Settings - All configuration options
• Environment Variables - Environment variable references
• Installation Guide - Detailed installation instructions

For performance benchmarks and technical details, refer to BENCHMARKS.md in the source code.

For troubleshooting common issues, see the Troubleshooting Guide.

The complete source code and implementation details are available at source/uv/.

Instructions

Core UV Concepts

UV is a comprehensive Python tool that replaces:

• pip (package installation)
• pip-tools (dependency resolution)
• pipx (tool installation)
• poetry (project management)
• pyenv (Python version management)
• virtualenv (virtual environments)
• twine (package publishing)

Installation

Recommended installation methods:

# Standalone installer (macOS/Linux)
curl -LsSf https://astral.sh/uv/install.sh | sh

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

# Via pip
pip install uv

# Via pipx
pipx install uv

Project Management

Initialize new projects:

# Create new project
uv init my-project
cd my-project

# Create with specific Python version
uv init my-project --python 3.11

# Create from existing directory
uv init

Dependency management:

# Add dependencies
uv add requests pandas

# Add development dependencies
uv add --dev pytest black

# Add with specific version
uv add "requests>=2.28.0"

# Remove dependencies
uv remove requests

# Update dependencies
uv lock --upgrade

Project execution:

# Run commands in project environment
uv run python main.py
uv run pytest
uv run black .

# Run scripts
uv run script.py

Virtual Environments

Environment management:

# Create virtual environment
uv venv

# Create with specific Python version
uv venv --python 3.11

# Activate environment (standard approach)
source .venv/bin/activate  # Unix
.venv\Scripts\activate     # Windows

# Use environment without activation
uv pip install requests
uv python script.py

Package Installation

** pip-compatible interface:**

# Install packages
uv pip install requests pandas

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

# Install with constraints
uv pip install -c constraints.txt

# Install in editable mode
uv pip install -e .

# Uninstall packages
uv pip uninstall requests

Python Version Management

Python version management:

# List available Python versions
uv python list

# Install Python version
uv python install 3.11

# Set Python version for project
uv python pin 3.11

# Use specific Python version
uv run --python 3.11 script.py

Tool Management

Install and manage tools:

# Install as tool
uv tool install ruff
uv tool install pytest

# Run tools
uv run ruff check .
uv run pytest

# List installed tools
uv tool list

# Uninstall tools
uv tool uninstall ruff

Scripts Support

Run scripts with inline dependencies:

# script.py with inline dependencies
# /// script
# requires-python = ">=3.8"
# dependencies = [
#     "requests",
#     "typer",
# ]
# ///

import requests
import typer

def main():
    response = requests.get("https://api.github.com")
    print(f"Status: {response.status_code}")

if __name__ == "__main__":
    typer.run(main)

# Run script with automatic dependency resolution
uv run script.py

Advanced Features

Workspaces:

# Create workspace
# pyproject.toml
[tool.uv.workspace]
members = ["packages/*"]

# Work with workspace
uv add requests --workspace
uv sync --workspace

Caching:

# Clear cache
uv cache clean

# View cache info
uv cache info

# Warm cache
uv sync --cache-dir ~/.cache/uv

Configuration:

# Set global configuration
uv config set global.cache-dir ~/.cache/uv

# View configuration
uv config show

# Use configuration file
# ~/.config/uv/uv.toml
[global]
cache-dir = "~/.cache/uv"
index-url = "https://pypi.org/simple"

Common Workflows

1. Set up new project:

uv init my-project
cd my-project
uv add requests pytest --dev
uv run pytest

2. Work on existing project:

git clone <repo>
cd <repo>
uv sync
uv run python main.py

3. Migrate from pip:

# Replace pip install
uv pip install package

# Replace requirements.txt workflow
uv add package  # Adds to pyproject.toml
uv lock         # Creates lockfile

4. CI/CD integration:

# Install dependencies
uv sync

# Run tests
uv run pytest

# Build package
uv build

Performance Benefits

UV is 10-100x faster than pip:

• Parallel downloads and installations
• Efficient caching
• Rust-based performance
• Global dependency deduplication

Use UV when you need:

• Faster dependency resolution
• Unified Python toolchain
• Modern Python project management
• Better caching and performance
• Cross-platform consistency

Migration Guides

From pip:

• Replace pip install with uv add (for projects) or uv pip install (standalone)
• Replace pip freeze with uv pip freeze or use uv.lock
• Replace venv with uv venv

From poetry:

• pyproject.toml format is compatible
• Replace poetry add with uv add
• Replace poetry install with uv sync
• Replace poetry run with uv run

From conda:

• Use UV for Python packages, conda for system packages
• Migrate environment.yml to pyproject.toml
• Use uv venv instead of conda environments

Best Practices

1. Project Structure:

my-project/
├── pyproject.toml
├── uv.lock
├── src/
└── tests/

2. Dependency Management:

• Use uv add for project dependencies
• Use uv add --dev for development dependencies
• Keep uv.lock in version control
• Use uv sync to reproduce environments

3. Virtual Environments:

• Always use virtual environments
• Use uv run instead of manual activation
• Delete .venv when switching Python versions

4. Performance:

• Let UV manage the cache automatically
• Use workspaces for multi-package projects
• Enable parallel operations where possible

Troubleshooting

Common issues:

• Cache corruption: Run uv cache clean
• Python version conflicts: Use uv python pin
• Network issues: Check uv config get index-url
• Permission errors: Use user installations or virtual environments

Debug mode:

# Enable verbose output
uv --verbose sync

# Show resolution details
uv add package --verbose

Integration Examples

With Docker:

FROM python:3.11-slim
COPY uv.lock pyproject.toml ./
RUN pip install uv && uv sync
COPY . .
CMD ["uv", "run", "python", "main.py"]

With GitHub Actions:

- name: Install uv
  uses: astral-sh/setup-uv@v1
- name: Install dependencies
  run: uv sync
- name: Run tests
  run: uv run pytest

Resources

• Official documentation: https://docs.astral.sh/uv/
• GitHub repository: https://github.com/astral-sh/uv
• Benchmark comparisons: See BENCHMARKS.md in source
• Community Discord: https://discord.gg/astral-sh