uv for Python: A Fast, Modern Package Manager (Features, Setup & Limitations)

Python has gained massive popularity over the last several years across both personal and enterprise development thanks to its simplicity and massive ecosystem. But that rapid expansion has come with growing pains. Traditional tooling (such as pip, venv, and related package/environment managers) often struggles to keep pace, leading to slow installs, inconsistent environments, and dependency issues across machines.

Enter uv: a modern, all-in-one package and project manager that consolidates several commonly used Python tools into a single, fast, sleek solution. In this post, we’ll cover what uv is, why it may be an attractive option to both new and experienced python developers, where it shines, and where it still has limitations.

Here’s what we’ll explore:

• What uv is and why it’s gaining momentum with developers in the Python ecosystem
• How uv compares to pip, venv, and other standard Python tooling
• A quick example of using uv to set up and manage a new project
• uv’s current limitations and scenarios where it may not be the right choice

Traditional Python tooling can become slow or inconsistent across environments, creating friction for developers and teams. uv aims to solve these pain points with a fast, predictable, Rust-powered workflow that unifies package management, environment creation, and Python installation.

Let’s get started.

What is uv?

At its core, uv is a Python package and project manager designed for speed, reliability, and simplicity.

Because uv is written in Rust, many operations—such as environment creation, dependency resolution, and Python installation—are significantly faster than traditional tools like pip and venv. uv has also gained rapid adoption among developers focused on performance, DevOps, and modern tooling — areas we work in extensively through our Python development services.

Because uv runs independently of Python itself, it also offers several valuable advantages that go beyond what pip or venv can provide.

1. Zero prerequisites and frictionless installation

Because it is written in Rust, uv can be installed with essentially no prerequisites.

uv can be installed on any machine without worrying about Python versions or system-level conflicts.

2. A drop-in replacement for pip and venv

It is a drop-in replacement for pip and venv in most projects. Also, uv has compatibility with most existing pip and venv commands so even if you swap, you can mostly keep using the same commands as before. This doesn’t mean uv is a clone of pip but rather uv understands uplifts can be painful and tries to mitigate those issues as cleanly as possible.

uv supports the majority of pip/venv workflows and commands, making the migration process extremely low-friction. 

3. Consistent environment management across operating systems

In addition to being a drop-in replacement, uv is independent of Python itself so there are no conflicts whether you already have Python installed or not.

This separation from Python allows uv itself provides a unified way to install Python that is consistent across all OS’s without needing admin rights and provides the ability to install multiple python versions without conflicts. This also allows uv to minimize bootstrapping headaches when setting up Python projects across machines as there is a unified workflows across all OS.

uv can install multiple Python versions side-by-side, without admin rights, using a unified workflow across macOS, Windows, and Linux.

4. Better dependency management and project organization

Furthermore, uv provides many helpful project management features on top of improved package management. These features simplify the development process and respect the developer’s time. This includes things like dependency lock files, simplified project and venv creation, and consistent behavior across different environments.

Dependency management has also been improved with uv by providing clean dependency lists. This is done by only storing direct dependencies in the pyproject.toml and leveraging commands such as uv add and uv remove to simplify dependency changes. This design combined with a detailed lockfile means anyone can replicate the exact same environment using uv in seconds.

uv introduces:

• Dependency lock files
• Clean pyproject.toml-based configuration
• Automatic virtual environment creation
• Commands like uv add and uv remove for simpler dependency updates

The combination of dependency isolation, lockfiles, and environment consistency means anyone can recreate the same Python environment in seconds, regardless of OS.

Here’s a quick side-by-side comparison of uv and the traditional pip/venv tooling:

Using uv: A Quick Example

Now that we have seen some of the ways uv can help with Python project management, lets dive into an example of setting up a Python project from scratch with uv.

1. Install uv

First we will install uv:

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

We can check that uv was installed correctly by running uv --help.

2. Initialize a new Python project

Once uv is installed it is time to create a new Python project, which is as simple as running the following command.

uv init my_project_name

Once your project is initialized you will notice that uv automatically provides the basic files that most Python projects would need. This includes a .gitignore, pyproject.toml, README.md, .python-version and a main.py hello world script.

These are all important but the main focus should be on the pyproject.toml as that is where a lot of the “magic” happens.

3. Add or remove dependencies

Now that we have a project base we can add or remove dependencies as needed by using the following commands:

uv add <dependency name>
uv remove <dependency name>

For this example I will add ruff as a dev dependency (uv add ruff --dev) and fastapi as a main dependency (uv add fastapi).

Running these commands will update your pyproject.toml file to have the following:

dependencies = [
    "fastapi<=0.122.1",
]

[dependency-groups]
dev = [
    "ruff<=0.14.7",
]

In addition to updating the pyproject.toml to track your dependencies, uv will also automatically generate a .venv environment if one was not already setup and create a uv.lock lockfile with all of the dependency requirements at the same time. This means no more tedious dependency management as uv handles it internally for you.

Furthermore, since uv manages dependencies within the pyproject.toml file, a requirements.txt file is no longer needed as all the dependency information can be understood from the pyproject.toml file and the uv.lock file.

4. Sync dependencies

Now that we have a fully working example with a couple of basic dependencies, there are a couple more commands that will be useful during development.

First is when you initially pull down a project that uses uv, you will need to install the dependencies. This is as simple as running uv sync.

5. Run Python scripts with uv

In addition, uv supports running Python scripts directly with uv run .

6. Install Python versions directly through uv

Finally, while uv will automatically detect and use the Python version installed on your machine, it also supports downloading Python directing by using the command uv python install.

This is especially helpful when setting up builds in completely new environments for things like pipeline actions as installing uv is the only prerequisite before using uv for everything else.

7. Pip migration

Thanks to uv being essentially a drop-in replacement from pip, it can leverage most of pip’s requirements file/layout which makes migration generally a breeze.

First generate a pyproject.toml with  uv init (if your project doesn’t already contain one) and then run uv add -r requirements.in to import your requirements based on your requirements file.

Limitations of uv

As with most things, there is not always a perfect solution. While uv can do a lot, it still has a few limitations.

1. Legacy pip behavior may break

The first limitation of uv is that if you are working on a legacy project that uses pip’s old and more lenient package resolution, uv can break dependency management due to compatibility issues.

2. Command-line only

Secondly, uv is a command line only interface. While this might not seem like a problem, python gets a fair bit of use in the scientific community where not having a GUI may be a large drawback. In those scientific and data science workflows, using something like Conda may be better.

3. Global cache size can grow

Another key limitation of uv package manager is this: uv uses a global dependency cache, and  installing many large packages across many projects can cause the cache to grow over time.

While uv’s global cache prevents dependency duplication, large volume installs with different dependencies can lead to a hidden ballooning cache size over time. This bloat can be resolved by periodically clearing the uv cache (uv cache clean) and forcing a redownload – but it is still something to note.

Who Should Use uv?

uv is an excellent choice for Python developers who want fast environment creation, reproducible builds, and a unified workflow across operating systems.

Teams working with CI/CD pipelines, containerized builds, or distributed engineering groups will especially benefit from uv’s consistent dependency resolution and lockfile-based reproducibility.

If you manage multiple Python versions, frequently onboard new developers, or maintain many virtual environments, uv can streamline almost every part of your workflow.

uv vs pip: When you shouldn’t switch yet

While uv is extremely fast and feature-rich, it is still evolving. You may want to postpone adoption if your team:

• Requires strict, long-term reproducibility for regulated environments
• Maintains legacy projects with pip’s older dependency behavior
• Relies heavily on tooling not yet fully supported by uv

In these cases, pip or Conda may still be the safer choice until uv matures further.

Conclusion

We have just scratched the surface of what uv can do. It is quickly becoming one of the most compelling tools in the Python ecosystem, especially for developers who value:

• Faster installs
• Cleaner dependency management
• Easier environment setup
• Cross-platform consistency
• Reduced tooling overhead

Now that we have gone through uv and seen some the of the things it can help manage, I encourage you to try it for yourself the next time you find yourself working with Python.

While there may be use cases where uv isn’t the right fit, overall I believe it is strongly worth considering in modern python development scenarios due to its many features and support… as well as its simplicity in getting a project up in running.

Takeaway: uv shines as a fast, modern Rust-powered package manager that improves Python productivity, dependency management, and environment consistency. uv is absolutely worth exploring for new projects or teams seeking to streamline their workflow.

If you found this dive into uv helpful and want to explore other topics like Python tooling, cloud engineering, or modern development practices, the Keyhole Software Dev blog is a great place to start for additional tutorials, best practices, and engineering insights.

If you’re exploring uv as part of a broader modernization or DevOps process (improving Python workflows, accelerating builds, or reducing environment inconsistencies) Keyhole Software has helped many teams adopt modern tooling and workflows. Reach out if you’d like support evaluating uv or improving your Python engineering environment.