The people shaping uv’s 2026 Python packaging ecosystem

uv dominance in CI

uv pulls 75 million monthly downloads on PyPI, which exceeds Poetry’s 66 million. Astral’s Rust-based tool completes a cold install from a lock file in 4.2 seconds, while Poetry takes 52.1 seconds and pip-tools takes 68.4 seconds. This speed advantage matters most in CI/CD pipelines where every second costs money. I observe the difference most when a project with 42 dependencies runs on a GitHub Actions runner. In that scenario, uv finishes in 4 seconds while Poetry requires 60 seconds. The difference between uv and pip-tools during a cold install from a lock file measures over 64 seconds, a gap that translates into significant time and cost savings during every single CI/CD pipeline run. Cloudflare uses uv to power its Python Workers, employing a new CLI tool called pywrangler to handle module bundling for the edge. This integration helps heavy packages like FastAPI and Pydantic load in 1 second instead of 10 seconds. Cloudflare’s Python Workers also start 2.4 times faster than AWS Lambda and 3 times faster than Google Cloud Run. Meanwhile, pip 26.1 introduced dependency cooldowns to prevent supply chain attacks by requiring a seven-day waiting period for freshly published packages. pip also added experimental support for pylock.toml files, which allows it to install from the same lockfiles that uv uses. The pip release also includes security patches for CVE-2026-3219 and CVE-2026-6357, and it drops support for Python 3.9.

Why Poetry still holds territory

Astral built uv in Rust to replace pip, pip-tools, and virtualenv. The tool runs 8 to 10 times faster than pip without a cache and up to 115 times faster with a warm cache. It uses a global module cache and leverages hardlinks to minimize disk space. If you already use pip, the migration to uv pip install requires zero configuration changes. You likely already know that managing Python environments often feels like a chore. uv also replaces pyenv by managing Python versions directly via the uv python install command. The uv resolver uses the PubGrub algorithm and allows for alternate resolution strategies, such as using the –resolution=lowest flag to test against the lowest compatible versions. uv also allows for dependency overrides to bypass erroneous upper bounds. The uv virtual environment creator runs 80 times faster than python -m venv and 7 times faster than virtualenv. Some developers express concern regarding Astral’s corporate ownership by OpenAI. They worry that leaning too heavily on a single corporate entity might limit long-term control. uv also lacks support for legacy features such as .egg distributions. Poetry remains a strong choice for library maintainers. It manages dependencies, virtual environments, building, and publishing through a unified interface. Poetry also handles dependency groups like dev, test, and docs more sophisticatedly than uv. It uses poetry.lock to pin every package by version and hash. However, Poetry’s installer inherits the performance ceiling of Python, making it much slower than Rust-based alternatives. For a large Django application with 80 transitive dependencies, the installation can stretch to several minutes.

Picking the right tool

Teams with existing requirements.txt workflows often stick with pip-tools. This tool adds pip-compile and pip-sync to the standard pip functionality. It generates a pinned requirements.txt that includes every transitive dependency. Libraries like Daggr, which the Gradio team released for inspecting AI workflows, can be installed via pip or uv for Python 3.10 and newer. Daggr allows for programmatic workflow definitions with node types like GradioNode, FnNode, and InferenceNode. Large monorepos like Apache Airflow use uv alongside pyproject.toml to manage over 100 sub-packages. This approach allows all workspace members to share a single uv.lock file for consistent versions across every service. I wonder if the complexity of managing such a massive codebase will eventually drive teams back toward more traditional, slower tools.

The choice between these tools depends on whether a team prioritizes speed, maturity, or minimal changes. I recommend uv for new projects and CI/CD optimization. I recommend Poetry for developers publishing libraries and pip-tools for maintaining legacy environments.