For most of Python's history, dependency management was a series of compounding workarounds — pip for packages, virtualenv for isolation, pipenv for convenience that came with its own bugs, poetry for a better experience that introduced its own learning curve. In 2025, Astral shipped uv — a Rust-based, all-in-one Python package manager — and by early 2026 it has become the obvious default for new Python projects. The speed difference is real and the migration is nearly frictionless.
This guide covers what uv actually does, the key commands, a realistic comparison to the alternatives, and the one place where it still has rough edges.
What uv is
uv is a Python package and project manager written in Rust and maintained by Astral (the same team behind Ruff). It handles the full stack that previously required multiple tools:
- Package installation (
pip equivalent)
- Virtual environment creation (
virtualenv equivalent)
- Dependency locking (
poetry lock equivalent)
- Script running (
python -m equivalent)
- Python version management (replaces pyenv for most use cases)
One binary. One lockfile (uv.lock). No background daemon.
Why it's faster
The speed advantage comes from three sources:
Parallel resolution. uv resolves dependencies in parallel using a PubGrub-based algorithm. pip resolves sequentially. On a project with 30+ dependencies this difference is measured in minutes, not seconds.
Aggressive caching. uv caches at the wheel level globally across projects. If you've ever installed requests before, a fresh project installs it in milliseconds. pip's cache is less complete and less aggressive.
Rust vs Python. The resolution and installation hot path is compiled Rust. No GIL, no interpreter overhead, minimal memory allocation.
In practice: a fresh install of a typical data science stack (pandas, numpy, scikit-learn, matplotlib) takes ~25 seconds with uv and 4–8 minutes with pip on a cold CI runner.
The key commands
# Install uv
curl -Ls https://astral.sh/uv/install.sh | sh
# Create a virtual environment
uv venv
# Install a package (respects existing venv)
uv pip install requests
# Install from requirements.txt
uv pip install -r requirements.txt
# Initialize a project with pyproject.toml
uv init myproject
# Add a dependency (writes to pyproject.toml + uv.lock)
uv add pandas
# Lock dependencies
uv lock
# Run a script in the project environment
uv run python main.py
# Sync environment to lockfile (CI-ready)
uv sync
For most daily work, uv add, uv sync, and uv run replace the pip + virtualenv dance entirely.
Replacing the old stack
The typical migration path by prior tool:
pip + virtualenv → uv pip install + uv venv. Drop-in replacement, zero friction. Your existing requirements.txt works unchanged.
poetry → uv init + uv add. Migration takes 10–20 minutes: copy your [tool.poetry.dependencies] section, run uv add for each package, delete poetry.lock, run uv lock. The pyproject.toml format is compatible.
pipenv → uv sync from a converted requirements.txt. Pipenv's lockfile format differs; export with pipenv requirements first.
conda → Partial. uv handles Python packages but not conda-specific packages (CUDA, native libraries tied to conda channels). For pure-Python projects, uv is a complete replacement; for scientific computing with CUDA, conda still has a role.
Comparison: uv vs pip vs poetry vs conda
| Dimension |
uv |
pip + virtualenv |
poetry |
conda |
| Install speed |
Very fast (10–100×) |
Slow |
Moderate |
Slow |
| Lock file |
Yes (uv.lock) |
No (freeze) |
Yes (poetry.lock) |
Yes (environment.yml) |
| Virtual env |
Built-in |
Separate tool |
Built-in |
Built-in |
| Workspace support |
Yes |
No |
Yes |
No |
| Python version mgmt |
Yes (uv python) |
No |
Limited |
Yes |
| Learning curve |
Low |
Very low |
Moderate |
Moderate |
| Non-Python packages |
No |
No |
No |
Yes |
| Best for |
New Python projects |
Legacy scripts |
Complex mono-repos |
Scientific computing with CUDA |
The one gotcha
Some packages with C extensions — particularly older scientific or system-level packages — distribute only source distributions (sdist) rather than pre-built wheels. pip's build system is more battle-tested for these edge cases in 2026. If you hit a package that fails to build under uv with a cryptic Rust or build error, falling back to pip install for that specific package while keeping uv for everything else is a fine interim solution.
Packages that most commonly cause issues: some older versions of psycopg2 (use psycopg2-binary instead), some custom internal packages built against specific system libraries, niche scientific packages with Fortran dependencies.
Common mistakes to avoid
Not committing uv.lock. The lockfile is the point of reproducibility. If it's gitignored, your team doesn't get deterministic installs.
Running uv pip install without a venv. uv will install to the system Python if no venv is active. Run uv venv first or use uv sync with a project-based workflow.
Assuming conda can be replaced entirely. If you need CUDA-specific binaries or conda-channel-only packages, keep conda for that layer. uv and conda can coexist.
FAQ
Is uv production-ready in 2026?
Yes — it's used in production by hundreds of teams and is the recommended tool in several major Python project templates. The 1.0 release shipped in late 2025.
Does uv work with GitHub Actions and other CI systems?
Yes. The recommended CI pattern is uv sync --frozen (installs exactly what's in uv.lock, errors if the lockfile is outdated). Most major CI providers have uv setup actions.
Will uv break my existing project?
For most pure-Python projects, no — it reads requirements.txt and pyproject.toml formats that pip and poetry write. Test on a branch before switching a production project.
Where to go next
For more Python tooling guidance see how to learn Python in 2026, Node.js vs Deno vs Bun in 2026, and best CLI tools for developers in 2026.