Using workspaces
Inspired by the Cargo concept of the same name, a workspace is "a collection of one or more packages, called workspace members, that are managed together."
Workspaces organize large codebases by splitting them into multiple packages with common dependencies. Think: a FastAPI-based web application, alongside a series of libraries that are versioned and maintained as separate Python packages, all in the same Git repository.
In a workspace, each package defines its own pyproject.toml, but the workspace shares a single
lockfile, ensuring that the workspace operates with a consistent set of dependencies.
As such, uv lock operates on the entire workspace at once, while uv run and uv sync operate on
the workspace root by default, though both accept a --package argument, allowing you to run a
command in a particular workspace member from any workspace directory.
Getting started
To create a workspace, add a tool.uv.workspace table to a pyproject.toml, which will implicitly
create a workspace rooted at that package.
Tip
By default, running uv init inside an existing package will add the newly created member to the workspace, creating a tool.uv.workspace table in the workspace root if it doesn't already exist.
In defining a workspace, you must specify the members (required) and exclude (optional) keys,
which direct the workspace to include or exclude specific directories as members respectively, and
accept lists of globs:
[project]
name = "albatross"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = ["bird-feeder", "tqdm>=4,<5"]
[tool.uv.sources]
bird-feeder = { workspace = true }
[tool.uv.workspace]
members = ["packages/*"]
exclude = ["packages/seeds"]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
Every directory included by the members globs (and not excluded by the exclude globs) must
contain a pyproject.toml file. However, workspace members can be either
applications or libraries; both are supported in
the workspace context.
Every workspace needs a root, which is also a workspace member. In the above example, albatross
is the workspace root, and the workspace members include all projects under the packages
directory, with the exception of seeds.
By default, uv run and uv sync operates on the workspace root. For example, in the above
example, uv run and uv run --package albatross would be equivalent, while
uv run --package bird-feeder would run the command in the bird-feeder package.
Workspace sources
Within a workspace, dependencies on workspace members are facilitated via
tool.uv.sources, as in:
[project]
name = "albatross"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = ["bird-feeder", "tqdm>=4,<5"]
[tool.uv.sources]
bird-feeder = { workspace = true }
[tool.uv.workspace]
members = ["packages/*"]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
In this example, the albatross project depends on the bird-feeder project, which is a member of
the workspace. The workspace = true key-value pair in the tool.uv.sources table indicates the
bird-feeder dependency should be provided by the workspace, rather than fetched from PyPI or
another registry.
Note
Dependencies between workspace members are editable.
Any tool.uv.sources definitions in the workspace root apply to all members, unless overridden in
the tool.uv.sources of a specific member. For example, given the following pyproject.toml:
[project]
name = "albatross"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = ["bird-feeder", "tqdm>=4,<5"]
[tool.uv.sources]
bird-feeder = { workspace = true }
tqdm = { git = "https://github.com/tqdm/tqdm" }
[tool.uv.workspace]
members = ["packages/*"]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
Every workspace member would, by default, install tqdm from GitHub, unless a specific member
overrides the tqdm entry in its own tool.uv.sources table.
Workspace layouts
The most common workspace layout can be thought of as a root project with a series of accompanying libraries.
For example, continuing with the above example, this workspace has an explicit root at albatross,
with two libraries (bird-feeder and seeds) in the packages directory:
albatross
├── packages
│ ├── bird-feeder
│ │ ├── pyproject.toml
│ │ └── src
│ │ └── bird_feeder
│ │ ├── __init__.py
│ │ └── foo.py
│ └── seeds
│ ├── pyproject.toml
│ └── src
│ └── seeds
│ ├── __init__.py
│ └── bar.py
├── pyproject.toml
├── README.md
├── uv.lock
└── src
└── albatross
└── main.py
Since seeds was excluded in the pyproject.toml, the workspace has two members total: albatross
(the root) and bird-feeder.
When (not) to use workspaces
Workspaces are intended to facilitate the development of multiple interconnected packages within a single repository. As a codebase grows in complexity, it can be helpful to split it into smaller, composable packages, each with their own dependencies and version constraints.
Workspaces help enforce isolation and separation of concerns. For example, in uv, we have separate packages for the core library and the command-line interface, enabling us to test the core library independently of the CLI, and vice versa.
Other common use cases for workspaces include:
- A library with a performance-critical subroutine implemented in an extension module (Rust, C++, etc.).
- A library with a plugin system, where each plugin is a separate workspace package with a dependency on the root.
Workspaces are not suited for cases in which members have conflicting requirements, or desire a
separate virtual environment for each member. In this case, path dependencies are often preferable.
For example, rather than grouping albatross and its members in a workspace, you can always define
each package as its own independent project, with inter-package dependencies defined as path
dependencies in tool.uv.sources:
[project]
name = "albatross"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = ["bird-feeder", "tqdm>=4,<5"]
[tool.uv.sources]
bird-feeder = { path = "packages/bird-feeder" }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
This approach conveys many of the same benefits, but allows for more fine-grained control over
dependency resolution and virtual environment management (with the downside that uv run --package
is no longer available; instead, commands must be run from the relevant package directory).
Finally, uv's workspaces enforce a single requires-python for the entire workspace, taking the
intersection of all members' requires-python values. If you need to support testing a given member
on a Python version that isn't supported by the rest of the workspace, you may need to use uv pip
to install that member in a separate virtual environment.
Note
As Python does not provide dependency isolation, uv can't ensure that a package uses its declared dependencies and nothing else. For workspaces specifically, uv can't ensure that packages don't import dependencies declared by another workspace member.