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While exploring Mojo from Modular, I discovered Pixi (https://pixi.sh), a package manager I'd somehow missed. It excels at multi-language projects, like combining Python with Mojo or Rust. For my Python-only work, I've relied on uv from Astral (https://docs.astral.sh/uv/), paired with just (https://just.systems) for managing repeatable tasks and project documentation (see my earlier post).

Both tools are fast, Rust-based, and modernize dependency management. But as my projects evolve toward mixed languages (e.g., Python + Mojo), Pixi's integrated approach—handling environments, dependencies, and tasks in one tool—starts to look appealing, especially with its strong cross-platform support (Linux, macOS, Windows).

This two-part post compares them factually. Part 1 covers the basics and workflows; Part 2 dives into Pixi's advanced features.

What is uv?

uv is a unified tool replacing pip, virtualenv, Poetry, and more. It focuses on Python, sourcing exclusively from PyPI.

Key features:

• uv init scaffolds projects with pyproject.toml.
• uv add <package> manages dependencies and lockfiles.
• Automatic .venv creation; uv run executes in the environment.
• Seamless pip compatibility via uv pip.

uv prioritizes simplicity and speed for pure Python workflows.

What is Pixi?

Pixi (from Prefix.dev) modernizes conda for multi-language projects. It pulls from conda-forge (binaries/non-Python) and PyPI—using uv under the hood for Python resolution since 2024.

Key features:

• pixi init creates pixi.toml.
• pixi add <package> handles mixed dependencies.
• Reproducible environments with pixi.lock.
• Built-in task runner for automation.

Pixi shines in cross-platform, multi-language setups.

Key Differences in Usability

uv feels lightweight for Python-only; Pixi adds power for projects with greater complexity.

Pros and Cons

uv Pros:

• Aligns with Python ecosystem.
• Minimal overhead.
• Excellent for scripts/apps/libraries.

uv Cons:

• No non-PyPI or multi-lang support.
• No built-in tasks.

Pixi Pros:

• Seamless mixed deps (e.g., Mojo binaries).
• Task integration reduces tool juggling.
• Strong reproducibility.

Pixi Cons:

• New config format.
• Potential overhead for simple projects.

Workflow Examples (Python-Only)

Simple Script

uv: uv init → uv add requests → uv run python script.py
Pixi: pixi init → pixi add requests → pixi run python script.py
Similar, but uv uses familiar standards.

Data Science (Pandas + Jupyter)

uv: Add deps → uv run jupyter lab
Pixi: Add deps → define task notebook: jupyter lab → pixi run notebook
Pixi tasks make repeats easier.

Web App (FastAPI)

Similar adds/runs, but Pixi tasks shine for dev: uvicorn --reload.

For Python-only, uv + just is ergonomic. Pixi integrates tasks, reducing tools.

When to Choose Which

Stick with uv for pure Python. Switch to Pixi for multi-language (e.g., Python/Mojo) or when built-in tasks appeal—especially as projects grow.

Next: Part 2 explores Pixi's tasks (vs. just), integrations, and pixi.toml for data science.