Meet uv: A Fast, All‑in‑One Toolchain for Modern Python Projects

If you’ve been writing Python for a while, your toolbox probably looks like this:

• pyenv to manage Python versions
• virtualenv or venv for virtual environments
• pip + pip-tools for installing and locking dependencies
• Maybe pipx for CLI tools
• Maybe Poetry for project management and publishing

It works… but it’s a lot.

uv, a new tool from Astral (the team behind Ruff), tries to collapse all of that into one blazing‑fast binary. It’s written in Rust, it’s insanely quick, and it’s designed to feel familiar if you already know pip.

In this post, we’ll look at what uv is, why people are excited about it, and how you can start using it in your own projects.

What is uv?

uv is a high‑performance Python package and project manager. It aims to be a "Cargo for Python": one tool that can handle:

• Installing and resolving dependencies
• Managing virtual environments
• Managing Python versions
• Running scripts and tools
• Locking dependencies for reproducible builds

All of this comes in a single, standalone binary, with no direct dependency on an existing Python installation.

According to the official docs, uv can often be 10–100× faster than pip when installing dependencies, especially with a warm cache. It uses a global cache and copy‑on‑write tricks at the filesystem level to avoid re‑downloading and re‑building packages over and over again.

Highlights at a Glance

Here’s the elevator pitch for uv:

• 🧰 One tool to replace many It can take over jobs normally handled by pip, pip-tools, pipx, virtualenv, pyenv, poetry (for some workflows), and even publishing tools like twine.

• ⚡ Seriously fast installs Benchmarks from Astral show uv beating pip and pip-tools by an order of magnitude or more when resolving and installing dependencies, especially with a warm cache.

• 📦 Pip‑compatible interface You can keep using familiar commands like uv pip install, uv pip sync, and uv pip compile. That makes it easy to try uv in existing projects.

• 🔒 Universal lockfile uv can generate a lockfile that describes your full dependency graph, giving you reproducible builds across machines and environments.

• 🐍 Python version manager built in You can install, list, and pin Python versions directly through uv, similar to pyenv—but using the same binary you’re already using for packages.

• 🧪 Script runner with inline dependencies uv can run scripts that declare their dependencies in a comment block at the top of the file. That makes it easier to share small tools or examples without a whole project skeleton.

If you’re tired of juggling five different tools just to get a project running, uv is designed specifically to simplify that experience.

Why Should You Care?

Let’s zoom in on the benefits from a day‑to‑day developer perspective.

1. Speed

If you work in Python professionally, you probably:

• Create and destroy virtual environments frequently
• Reinstall dependencies in CI/CD pipelines
• Add or update packages regularly

Every time you do that, traditional tooling spends time:

• Downloading packages
• Resolving dependency versions
• Building wheels

uv tackles this with:

• An efficient dependency resolver written in Rust
• A global cache shared across environments
• Copy‑on‑write and hardlink tricks to reuse files

In practice, this means things like:

uv pip install -r requirements.txt

can feel dramatically faster than pip install -r requirements.txt, especially on repeated runs.

2. Fewer Tools to Keep in Your Head

Instead of:

• pyenv for versions
• python -m venv for virtualenvs
• pip + pip-tools for dependencies
• pipx for global CLI tools

…you can lean on uv for most of that:

• uv python for versions
• uv venv for environments
• uv pip or uv add for dependencies
• uv tool for installing and running Python‑based CLIs

Less cognitive overhead, fewer random "which command am I supposed to use here?" moments.

3. Reproducible Environments

Reproducibility is a pain point in Python. You might have:

• A requirements.txt that drifts over time
• Slightly different dependency graphs on each machine
• Subtle bugs that only appear in production

With uv, you can maintain a lockfile that captures exactly which versions are used. That makes it easier to:

• Debug issues (“we’re all really running the same versions, right?”)
• Rebuild environments in CI/CD
• Roll back to a known‑good state

4. A Future‑Facing Ecosystem

uv comes from the same ecosystem as Ruff, another Rust‑powered tool that’s become the default linter/formatter for many teams. There’s a clear trend here: performance‑focused tooling that respects existing Python standards but pushes them forward.

If you’re already using Ruff, uv fits nicely into that stack.

Installing uv

You can install uv as a standalone binary on all major platforms.

macOS & Linux (shell installer)

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

If you want it to install into a system‑wide location, you can prepend sudo:

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

Windows (PowerShell)

Open PowerShell as Administrator and run:

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

Homebrew

If you’re on macOS and already using Homebrew:

brew install uv

After installation, you can confirm that everything works with:

uv --version

Basic uv Workflow: From Zero to Running Code

Let’s walk through a simple workflow using uv in a new project.

1. Initialize a Project

Create and enter a new directory, then run:

mkdir hello-uv
cd hello-uv
uv init

uv init will:

• Set up a basic project structure
• Create a pyproject.toml
• Set up a virtual environment

You now have a ready‑to‑hack Python project.

2. Add Dependencies

You can add packages with uv add:

uv add requests

This will:

• Resolve the correct version of requests
• Update your dependency metadata and lockfile
• Install the package into the project’s virtual environment

Need multiple packages?

uv add fastapi uvicorn

3. Run Your Code

Instead of calling python main.py directly, you can do:

uv run main.py

uv makes sure:

• The correct virtual environment is active
• Dependencies are available

This is especially handy in CI or in shell scripts where activating a venv explicitly is annoying.

4. Manage Python Versions

You can see which Python versions uv knows about:

uv python list

To install or select a specific version for your project:

uv python pin 3.12

If that version isn’t available locally, uv will download and install it, then update your environment to use it.

Using uv With Existing pip Projects

You don’t have to rewrite your whole workflow to try uv. If you already have a requirements.txt, you can:

uv pip install -r requirements.txt

This works a lot like pip install -r requirements.txt, but with uv’s performance and caching.

You can also generate a lockfile from a requirements.in (or similar) using uv pip compile, in a way that feels familiar if you’ve used pip-tools.

Some quick mental mappings:

You can start with just uv pip and gradually adopt more of the higher‑level features.

Scripts With Inline Dependencies

One of the more fun features in uv is the ability to declare dependencies inside the script itself and let uv handle the rest.

For example, imagine a file called weather.py:

#!/usr/bin/env -S uv run
# /// script
# dependencies = ["httpx"]
# ///

import httpx

resp = httpx.get("https://example.com")
print(resp.status_code)

You can just run:

./weather.py

uv will:

• Notice the uv run shebang
• Read the inline dependency metadata
• Ensure httpx is installed and cached
• Run the script in an isolated environment

This makes it much easier to share small tools, demos, or one‑off utilities without:

• Creating a virtual environment manually
• Maintaining a separate requirements.txt

Where uv Fits in the Python Tooling Landscape

uv isn’t the only option for Python project management, but it’s one of the most ambitious ones right now.

Roughly speaking:

• If you just want to manage packages and environments and you’re used to pip + virtualenv, uv can be a drop‑in upgrade with better performance.
• If you’re using Poetry for full project management and publishing, uv can cover much of that surface area while staying closer to traditional Python packaging standards.
• If you’re using pyenv, pipx, and other small tools around the edges, uv can replace many of them with a single executable.

You don’t have to move everything at once. The nice thing about uv is that it’s modular:

• Use it just as a faster installer (uv pip)
• Or just as a version manager (uv python)
• Or go all‑in and let it handle your whole project lifecycle

Final Thoughts

The Python ecosystem has been slowly converging toward better tooling: pyproject.toml, modern build backends, Ruff, and now uv as a serious contender for package and project management.

If you:

• Care about install speed
• Are tired of juggling too many tools
• Want reproducible environments without a ton of ceremony

…then uv is absolutely worth a look.

The best way to evaluate it is simple:

1. Install uv
2. Point it at an existing project
3. Replace a couple of pip commands with uv pip

If you like the experience (and the speed), you can gradually adopt more of what it offers. If not, you haven’t had to change your project structure or commit to anything drastic.

Either way, uv is a big step toward a more cohesive, modern Python development experience—and it’s likely to become a core part of many teams’ toolchains in the coming years.