How and Why to Get Started with UV
Origins of UV
Python is a simplistic and readable programming language that has been around since 1991. Over the years, tools like pip, virtualenv, pipenv, and poetry have aimed to address the issue of installing and reproducing Python environments more easily. All of these brought improvements, but recently, the tool UV has emerged.
Ever found yourself zoning out while waiting for a virtual environment to activate or packages to install? You’re not alone. UV aims to fix that, offering a faster, simpler, and all-in-one approach to Python dependency management. Let’s explore where UV comes from, how it works, what it’s great at, and what to watch out for.
In Python, dependencies are external libraries that add functionality and evolve over time. Managing these requirements properly helps ensure the right versions are used and prevents compatibility conflicts across development, testing, and production environments. Historically, this process has required multiple tools and careful coordination.
Why UV was created
UV was created in response to certain frustrations in the Python workflows. Dependency installation with pip can be slow, environment setup often requires multiple tools, and projects suffer from differences between development, CI, and environments. Tools like pipenv and poetry attempted to bundle these concerns, but many developers found them either too slow or difficult to debug.
UV was developed by the Astral team. Their creation focuses on speed and simplicity. UV is written in Rust, which allows it to outperform other Python tooling while being compatible with the wide Python ecosystem. Bex Tuhchiev at Datacamp went on to describe the logistics of UV’s advance, “Through UV’s innovative architecture and efficient implementation, UV is 10 to 100 times faster than traditional package managers.” This is because of the speedy package installation, effortless integration with Python tools, built-in handling of virtual environments, optimized memory usage, reproducible environments using reliable dependency locking, and a design around modern Python packaging standards. As a result, UV offers a faster and more reliable approach to managing Python dependencies.
How UV is used
With UV, developers can generate environments, install dependencies, and run Python commands reliably and consistently.
A typical UV workflow starts with a pyproject.toml file. This acts as the single source of truth for a project’s dependencies. Instead of manually creating a virtual environment and then installing packages with pip, users can use UV commands to handle both steps smoothly. UV also supports dependency groups, which allow users to separate core production dependencies from development tools such as linters and testing frameworks. This structure holds production environments lightweight while still supporting development needs.
In CI, UV is notable for yielding faster and more reliable installs than traditional pip-based setups. Its efficient dependency resolution helps reduce build times, especially for larger projects. For individual coders, UV presents a more enjoyable experience when operating multiple projects. And teams can benefit from a more consistent environment setup.
Changes when adopting UV
With UV implementation, changes are inevitable. Several structural changes and workflow changes are required, particularly for teams transitioning from older management tools. Projects that previously relied on requirements.txt or Pipfile typically need to move their dependencies into a pyproject.toml file. While this migration takes initial effort, it centralizes dependency definitions and strengthens a single source of truth.
Another adjustment is how virtual environments are created and managed. Instead of manually constructing environments with virtualenv or relying on specific commands, UV handles environment creation automatically. Developers should adapt to running Python commands through UV, which will require updating documentation and habits.
On top of that, teams may modify runtime dependencies versus development dependencies. UV encourages a clearer separation between the two. This improves deployment visibility, though it requires teams to adjust to new conventions.
Benefits of using UV
One of UV’s most important advantages is speed. Because UV is written in Rust and uses caching, UV installs dependencies much faster than pip. This saves time and helps teams get feedback quicker. Another major benefit is simplicity. UV reduces the demand for multiple tools by combining environment creation, dependency installation, and command execution into a single interface.
UV functions perfectly on several platforms. It centers workflows around pyproject.toml. This nudges teams away from using legacy files and towards a consistent and clear format. As well, UV integrates cleanly with existing tooling and remains compatible with pip-based packages. Coders can easily adopt UV incrementally, making the experimentation low-risk.
Drawbacks/Limitations
As a relatively new tool, UV does not yet have the same depth of community knowledge, tutorials, and Stack Overflow answers. Fewer online resources can make troubleshooting more challenging. Another limitation is the learning curve. While UV aims to simplify workflows, users experienced with pipenv or poetry might need time to adjust to the new commands.
In some cases, highly specialized or legacy projects with complicated dependency requirements may run into compatibility issues. In these situations, teams will have to invest additional effort in refactoring or continue using the current tooling.
UV Setup Guide (starting from Pipfile + setup.py)
Goal:
Replace Pipenv-based workflows (Pipfile, Pipefile.lock, pipenv install, pipenv run, etc) with uv’s project workflow (pyproject.toml, uv.lock, uv sync, uv run, etc) using official uv concepts and commands.
- Install UV
- Create the uv project files from the repo root. This creates a baseline pyproject.toml file that will be edited:
uv init
3. Convert Pipfile dependencies into pyproject.toml. From Pipfile, move
- [packages] -> pyproject.toml -> [project] dependencies = […]
- [dev-packages] -> pyproject -> [dependency-groups] dev = […] (or another group name)
- Example pyproject.toml structure:
[project]
name = "your-project-name"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
"requests>=2.31",
"pydantic>=2",
]
[dependency-groups]
dev = [
"pytest",
"ruff",
"mypy",
]
Tip: keep version specifiers you already have in Pipfile. If Pipfile had exact pins, you can keep them, or let uv lock them precisely in uv.lock
4. Handle setup.py (if the repo still uses it)
- Case 1 (building/publishing a package): Move metadata from setup.py into [project] in pyproject.toml, then confirm your build still works.
- Case 2 (just running the app code): Still keep a minimal [project] with dependencies and use uv purely for environment + installs. UV supports project.dependencies, even if the project is not published.
5. Generate the lockfile and install. This creates the virtual environment and uv.lock, which is the reproducible dependency resolution.
uv sync
6. Replace common Pipenv commands with uv commands:
pipenv install uv sync
pipenv install --dev uv sync (dev group present) / or uv sync --group dev
pipenv run python app.py uv run python app.py
pipenv run pytest uv run pytest
pipenv shell Activae .venv manually
7. Clean up old files
- Once uv.lock works and CI is updated, there will be removal of:
Pipfile
Pipfile.lock
… and update README scripts to use uv commands.
Conclusion
UV represents a reinvention of Python dependency management. By focusing on speed, simplicity, and modern standards, it addresses the annoyance that users have previously experienced. While it is not without limitations, its benefits make it a convincing option for new and dynamic projects.
As the Python ecosystem goes on evolving, tools like UV highlight a more extensive trend: developers demanding faster and more reliable workflows. UV does not just offer another way to install packages; it offers an insight of the future of Python development.