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You can try Astral’s uv with an existing requirements.txt by running uv pip install -r requirements.txt. For a new project, start with uv init, add packages with uv add, and use uv run or uv sync to work with the project environment. The often-quoted 2.46-second pip and 0.38-second uv figures are rounded results from one author’s specific benchmark—not a general speed guarantee.
Use uv with an existing requirements.txt
If your repository already uses requirements.txt, you can try uv’s pip-like interface without first converting the repository into a uv-managed project:
uv pip install -r requirements.txtThis is an installer substitution for that command. It does not, by itself, create a pyproject.toml, generate a uv.lock, or change your team’s project layout or CI configuration. Astral describes uv’s pip interface as a lower-level option for pip-style commands and existing workflows.
Check compatibility before changing a team workflow
uv supports common pip workflows, but Astral cautions that it does not reproduce every pip behavior exactly. If your process depends on less common pip options or behavior, test the relevant install, build, and CI paths before replacing pip broadly.
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Move an existing repository to a uv project when you are ready
Using uv pip install against a requirements file and migrating to uv’s project workflow are separate choices. For migration, Astral’s pip-to-project guide shows creating a pyproject.toml and importing dependencies with uv add -r requirements.in.
If preserving the versions currently selected by an existing requirements file matters, the guide shows using that file as a constraints input:
uv add -r requirements.in -c requirements.txtThat distinction matters: importing dependency declarations and keeping an existing resolution are not automatically the same operation. Decide whether your goal is to retain current pins or let uv resolve dependencies as part of the migration, then validate the resulting project and lockfile.
Start a new project with uv
For a new project, uv’s project workflow gives you a project configuration, a resolved dependency lockfile, and commands that manage the environment around them. A typical sequence is:
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Create a project:
uv init. -
Add dependencies, for example:
uv add requests rich. -
Run a project command, such as:
uv run python main.py. -
Synchronize the environment explicitly when needed:
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These commands follow Astral’s project guide. The project’s pyproject.toml declares its dependencies; uv.lock records the resolved dependency graph. Before each uv run, uv checks whether the lockfile is current with pyproject.toml and whether the environment matches the lockfile.
A published walkthrough demonstrates this sequence and shows uv sync recreating an environment after it has been removed. That terminal output is the author’s example, not a guaranteed timing or identical output for every project.
What the 2.46-second and 0.38-second figures measure
The headline numbers—pip at 2.46 seconds and uv at 0.38 seconds—are rounded figures from a DevLog article published September 26, 2026. The article reports both a terminal-recorded install run and a separate table of five-run medians; they are different measurements.
| Measurement | pip | uv | What it represents |
|---|---|---|---|
| Rounded headline figure | 2.46 s | 0.38 s | Rounded presentation of the article’s reported benchmark |
| Terminal-recorded install run | 2.643 s | 0.363 s | One displayed run in the article |
| Install median | 2.457 s | 0.381 s | Median of five runs per tool in the article’s table |
The first table’s test setup was a Mac mini M4 Pro running macOS 26.6.1, with Python 3.14.6, pip 26.1.2, and uv 0.11.6. The workload contained eight named packages, and the author disabled caches on both sides. These are the author’s reported conditions and results, not an independent benchmark or a promise for another machine, package set, Python version, cache state, or uv release.
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A later uv version was measured separately
The same author later reported a follow-up on the same machine using uv 0.12.11: pip’s install median was 2.338 seconds and uv 0.12.11’s was 0.375 seconds. The article notes that the virtual-environment timing method differs from its first table, so those results should not be merged with the original measurements as though they came from one unchanged test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret a pip-versus-uv comparison
Use these numbers as one controlled example, not as a forecast for your own install. A meaningful comparison needs the same workload and comparable timing methods. When judging a result, check:
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Workload: which packages and versions were installed.
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Cache state: whether both tools used warm caches or both ran with caches disabled.
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Environment: the machine, operating system, and Python version.
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Tool versions: the specific pip and uv releases tested.
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Timing method: whether the number is a single run, a median, or an end-to-end workflow that includes other steps.
For your own repository, the practical starting point is to run the install command against the same requirements file and environment you currently use, then check correctness and compatibility—not to assume the benchmark’s seconds will transfer directly.
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