What are the best alternatives to uv Python package manager in 2026?

Mar 20, 2026

I’ve been using uv for months now, and then the news dropped: OpenAI is acquiring Astral. My first reaction? A mix of excitement and concern. Like many developers on Reddit, I thought: “First they took mah’ RAM, then they took mah’ GPU, then they took mah’ SSD, but I’ll be dammed if they take my uv!”

But let’s be practical. With any acquisition, the future direction becomes uncertain. Will uv remain free? Will it pivot toward enterprise features? Will the open-source community lose influence? These are valid questions that pushed me to explore alternatives.

Why This Matters

Before diving into alternatives, let’s understand what made uv special:

10-100x faster package installation (Rust-based)
Unified tooling - package manager, venv manager, Python installer all in one
Drop-in pip replacement - minimal learning curve

But here’s the thing: some users reported uv has “stalled a bit recently, even some basic important issues have seen no progress.” So even before the acquisition, there were concerns.

The Alternatives Landscape

┌─────────────┬───────────┬─────────────┬───────────┬─────────────┐
│   Feature   │   uv      │   Poetry    │    pdm    │  pip+venv   │
├─────────────┼───────────┼─────────────┼───────────┼─────────────┤
│ Speed       │ ⚡ Fastest │    Medium   │    Fast   │    Slow     │
│ Maturity    │   New     │  Proven     │  Growing   │  Standard   │
│ Lockfile    │   Yes     │   Yes       │   Yes     │   Needs pip-tools │
│ venv mgmt   │   Built-in│   Built-in  │  PEP 582  │   Manual    │
│ Community   │  Growing  │   Large     │   Small   │   Huge      │
│ Future?     │ Uncertain │   Stable    │  Promising│   Stable    │
└─────────────┴───────────┴─────────────┴───────────┴─────────────┘

Poetry: The Battle-Tested Choice

Poetry is what I’d call the “safe bet.” It’s been around since 2018, has a large community, and handles the full lifecycle of a Python project.

Here’s how I migrated from uv to Poetry:

# First, export your current dependencies from uv
uv pip freeze > requirements.txt

# Create a new Poetry project (or initialize in existing)
poetry init

# Add dependencies (this can take a while for large projects)
cat requirements.txt | xargs poetry add

# Or manually add the important ones
poetry add requests flask sqlalchemy

# Add dev dependencies
poetry add pytest black ruff --group dev

# Install everything
poetry install

# Activate the virtual environment
poetry shell

The resulting pyproject.toml looks clean:

[tool.poetry]
name = "myproject"
version = "0.1.0"
description = "A Python project migrated from uv"
authors = ["Developer <[email protected]>"]

[tool.poetry.dependencies]
python = "^3.10"
requests = "^2.31.0"
flask = "^3.0.0"

[tool.poetry.group.dev.dependencies]
pytest = "^7.4.0"
black = "^23.0.0"
ruff = "^0.1.0"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

What I like:

Dependency resolution is solid (rarely get conflicts)
Built-in virtual environment management
pyproject.toml is the standard (no extra files)
Excellent documentation

What I miss from uv:

Speed (Poetry is noticeably slower on large projects)
The unified uv command was cleaner than remembering poetry add, poetry install, etc.

pdm: The Modern Alternative

If uv’s approach appealed to you, pdm might be the closest alternative. It supports PEP 582, which means no virtual environment needed - packages live in __pypackages__ inside your project.

# Install pdm
pip install pdm

# Initialize a project
pdm init

# Add dependencies
pdm add requests flask

# Add dev dependencies
pdm add pytest --dev

# Run scripts
pdm run python main.py

The workflow feels similar to uv, but the community is smaller. Documentation isn’t as comprehensive as Poetry’s.

The Traditional Path: pip + venv

Sometimes the simplest solution is the right one. If your project doesn’t need complex dependency resolution:

# Create virtual environment
python -m venv .venv

# Activate it
source .venv/bin/activate  # Linux/Mac
# or
.\.venv\Scripts\activate  # Windows

# Install packages
pip install requests flask

# For reproducibility, use pip-tools
pip install pip-tools
pip-compile requirements.in  # generates requirements.txt
pip-sync requirements.txt   # syncs your venv

This approach works well for:

Simple projects with few dependencies
Teams where everyone already knows pip
CI/CD pipelines that expect standard tooling

What About Pyflow?

Interestingly, in the Reddit discussion, one developer offered to “fix the bugs in Pyflow and get it updated.” Pyflow takes a similar unified approach to uv - it’s a single tool for Python version management, virtual environments, and packages.

# Install pyflow
pip install pyflow

# Initialize (creates pyproject.toml)
pyflow init

# Add dependencies
pyflow add requests

But here’s my honest take: Pyflow needs more maintainers. If you’re willing to contribute to an open-source project and help it grow, this could be a great option. But for production systems, I’d wait until it shows more activity.

Migration Checklist

If you decide to switch, here’s what I learned:

□ Export current dependencies (uv pip freeze > requirements.txt)
□ Test dependency resolution on your largest project first
□ Update CI/CD pipelines (cache paths will change)
□ Verify lockfile works across all environments
□ Update team documentation
□ Consider the learning curve for teammates
□ Plan for rollback (keep uv around for a bit)

Common Mistakes I’ve Seen

Switching without testing - Poetry and uv handle dependency resolution differently. A project that works with uv might have conflicts with Poetry’s resolver.
Ignoring lockfile differences - uv.lock and poetry.lock are not compatible. You can’t just rename one to the other.
Forgetting CI/CD - Your GitHub Actions or GitLab CI will need updates. The caching paths and install commands are different.
Not considering team familiarity - If your team knows pip but not Poetry, factor in the learning time.
Assuming identical behavior - Edge cases like platform-specific dependencies or complex version constraints might resolve differently.

My Recommendation

After testing these alternatives, here’s my decision matrix:

If you prioritize...          → Choose...
─────────────────────────────────────────────
Stability & community         → Poetry
Modern tooling (uv-like)      → pdm
Simplicity & compatibility    → pip + venv
Data science projects         → conda
Contributing to OSS           → Pyflow

For my production projects, I’m going with Poetry. The maturity and community support outweigh the speed difference. For quick prototypes, I might still use uv until its future direction becomes clearer.

The Reddit sentiment captures it well: “I refuse to go back” to the old ways. Modern Python packaging has improved dramatically, and regardless of which tool you choose, you’re better off than using raw pip with manual venv management.

The Bigger Picture

This situation with uv’s acquisition highlights something important about tool selection. When choosing core infrastructure tools, consider:

Governance model - Who controls the project?
Business model - How is it funded? Is it sustainable?
Community health - Active contributors, responsive maintainers
Migration path - How easy is it to switch away if needed?

These factors matter as much as technical features.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit: OpenAI to acquire Astral discussion
👨‍💻 Poetry - Python dependency management and packaging made easy
👨‍💻 PDM - A modern Python package manager
👨‍💻 Pyflow - An installation and dependency system for Python
👨‍💻 uv - An extremely fast Python package installer

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!