Creating Packages

Generating files for a package


The PkgTemplates package offers an easy, repeatable, and customizable way to generate the files for a new package. It can also generate files needed for Documentation, CI, etc. We recommend that you use PkgTemplates for creating new packages instead of using the minimal pkg> generate functionality described below.

To generate the bare minimum files for a new package, use pkg> generate.

(@v1.8) pkg> generate HelloWorld

This creates a new project HelloWorld in a subdirectory by the same name, with the following files (visualized with the external tree command):

shell> tree HelloWorld/
├── Project.toml
└── src
    └── HelloWorld.jl

2 directories, 2 files

The Project.toml file contains the name of the package, its unique UUID, its version, the authors and potential dependencies:

name = "HelloWorld"
uuid = "b4cd1eb8-1e24-11e8-3319-93036a3eb9f3"
version = "0.1.0"
authors = ["Some One <>"]


The content of src/HelloWorld.jl is:

module HelloWorld

greet() = print("Hello World!")

end # module

We can now activate the project by using the path to the directory where it is installed, and load the package:

pkg> activate ./HelloWorld

julia> import HelloWorld

julia> HelloWorld.greet()
Hello World!

For the rest of the tutorial we enter inside the directory of the project, for convenience:

julia> cd("HelloWorld")

Adding dependencies to the project

Let’s say we want to use the standard library package Random and the registered package JSON in our project. We simply add these packages (note how the prompt now shows the name of the newly generated project, since we activated it):

(HelloWorld) pkg> add Random JSON
   Resolving package versions...
    Updating `~/HelloWorld/Project.toml`
  [682c06a0] + JSON v0.21.3
  [9a3f8284] + Random
    Updating `~/HelloWorld/Manifest.toml`
  [682c06a0] + JSON v0.21.3
  [69de0a69] + Parsers v2.4.0
  [ade2ca70] + Dates

Both Random and JSON got added to the project’s Project.toml file, and the resulting dependencies got added to the Manifest.toml file. The resolver has installed each package with the highest possible version, while still respecting the compatibility that each package enforces on its dependencies.

We can now use both Random and JSON in our project. Changing src/HelloWorld.jl to

module HelloWorld

import Random
import JSON

greet() = print("Hello World!")
greet_alien() = print("Hello ", Random.randstring(8))

end # module

and reloading the package, the new greet_alien function that uses Random can be called:

julia> HelloWorld.greet_alien()
Hello aT157rHV

Defining a public API

If you want your package to be useful to other packages and you want folks to be able to easily update to newer version of your package when they come out, it is important to document what behavior will stay consistent across updates.

Unless you note otherwise, the public API of your package is defined as all the behavior you describe about public symbols. A public symbol is a symbol that is exported from your package with the export keyword or marked as public with the public keyword. When you change the behavior of something that was previously public so that the new version no longer conforms to the specifications provided in the old version, you should adjust your package version number according to Julia's variant on SemVer. If you would like to include a symbol in your public API without exporting it into the global namespace of folks who call using YourPackage, you should mark that symbol as public with public that_symbol. Symbols marked as public with the public keyword are just as public as those marked as public with the export keyword, but when folks call using YourPackage, they will still have to qualify access to those symbols with YourPackage.that_symbol.

Let's say we would like our greet function to be part of the public API, but not the greet_alien function. We could the write the following and release it as version 1.0.0.

module HelloWorld

export greet

import Random
import JSON

"Writes a friendly message."
greet() = print("Hello World!")

"Greet an alien by a randomly generated name."
greet_alien() = print("Hello ", Random.randstring(8))

end # module

Then, if we change greet to

"Writes a friendly message that is exactly three words long."
greet() = print("Hello Lovely World!")

We would release the new version as 1.1.0. This is not breaking because the new implementation conforms to the old documentation, but it does add a new feature, that the message must be three words long.

Later, we may wish to change greet_alien to

"Greet an alien by the name of \"Zork\"."
greet_alien() = print("Hello Zork")

And also export it by changing

export greet


export greet, greet_alien

We should release this new version as 1.2.0 because it adds a new feature greet_alien to the public API. Even though greet_alien was documented before and the new version does not conform to the old documentation, this is not breaking because the old documentation was not attached to a symbol that was exported at the time so that documentation does not apply across released versions.

However, if we now wish to change greet to

"Writes a friendly message that is exactly four words long."
greet() = print("Hello very lovely world")

we would need to release the new version as 2.0.0. In version 1.1.0, we specified that the greeting would be three words long, and because greet was exported, that description also applies to all future versions until the next breaking release. Because this new version does not conform to the old specification, it must be tagged as a breaking change.

Please note that version numbers are free and unlimited. It is okay to use lots of them (e.g. version 6.62.8).

Adding a build step to the package

The build step is executed the first time a package is installed or when explicitly invoked with build. A package is built by executing the file deps/build.jl.

julia> mkpath("deps");

julia> write("deps/build.jl",
             println("I am being built...")

(HelloWorld) pkg> build
  Building HelloWorld → `deps/build.log`
 Resolving package versions...

julia> print(readchomp("deps/build.log"))
I am being built...

If the build step fails, the output of the build step is printed to the console

julia> write("deps/build.jl",

(HelloWorld) pkg> build
    Building HelloWorld → `~/HelloWorld/deps/build.log`
ERROR: Error building `HelloWorld`:
ERROR: LoadError: Ooops
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] top-level scope
   @ ~/HelloWorld/deps/build.jl:1
 [3] include(fname::String)
   @ Base.MainInclude ./client.jl:476
 [4] top-level scope
   @ none:5
in expression starting at /home/kc/HelloWorld/deps/build.jl:1

A build step should generally not create or modify any files in the package directory. If you need to store some files from the build step, use the Scratch.jl package.

Adding tests to the package

When a package is tested the file test/runtests.jl is executed:

julia> mkpath("test");

julia> write("test/runtests.jl",

(HelloWorld) pkg> test
   Testing HelloWorld
 Resolving package versions...
   Testing HelloWorld tests passed

Tests are run in a new Julia process, where the package itself, and any test-specific dependencies, are available, see below.


Tests should generally not create or modify any files in the package directory. If you need to store some files from the build step, use the Scratch.jl package.

Test-specific dependencies

There are two ways of adding test-specific dependencies (dependencies that are not dependencies of the package but will still be available to load when the package is tested).

target based test specific dependencies

Using this method of adding test-specific dependencies, the packages are added under an [extras] section and to a test target, e.g. to add Markdown and Test as test dependencies, add the following to the Project.toml file:

Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test = ["Markdown", "Test"]

Note that the only supported targets are test and build, the latter of which (not recommended) can be used for any deps/build.jl scripts.

Alternative approach: test/Project.toml file test specific dependencies


The exact interaction between Project.toml, test/Project.toml and their corresponding Manifest.tomls are not fully worked out and may be subject to change in future versions. The older method of adding test-specific dependencies, described in the previous section, will therefore be supported throughout all Julia 1.X releases.

In Julia 1.2 and later test dependencies can be declared in test/Project.toml. When running tests, Pkg will automatically merge this and the package Projects to create the test environment.


If no test/Project.toml exists Pkg will use the target based test specific dependencies.

To add a test-specific dependency, i.e. a dependency that is available only when testing, it is thus enough to add this dependency to the test/Project.toml project. This can be done from the Pkg REPL by activating this environment, and then use add as one normally does. Let's add the Test standard library as a test dependency:

(HelloWorld) pkg> activate ./test
[ Info: activating environment at `~/HelloWorld/test/Project.toml`.

(test) pkg> add Test
 Resolving package versions...
  Updating `~/HelloWorld/test/Project.toml`
  [8dfed614] + Test
  Updating `~/HelloWorld/test/Manifest.toml`

We can now use Test in the test script and we can see that it gets installed when testing:

julia> write("test/runtests.jl",
             using Test
             @test 1 == 1

(test) pkg> activate .

(HelloWorld) pkg> test
   Testing HelloWorld
 Resolving package versions...
  Updating `/var/folders/64/76tk_g152sg6c6t0b4nkn1vw0000gn/T/tmpPzUPPw/Project.toml`
  [d8327f2a] + HelloWorld v0.1.0 [`~/.julia/dev/Pkg/HelloWorld`]
  [8dfed614] + Test
  Updating `/var/folders/64/76tk_g152sg6c6t0b4nkn1vw0000gn/T/tmpPzUPPw/Manifest.toml`
  [d8327f2a] + HelloWorld v0.1.0 [`~/.julia/dev/Pkg/HelloWorld`]
   Testing HelloWorld tests passed```

Compatibility on dependencies

Every dependency should in general have a compatibility constraint on it. This is an important topic so there is a chapter in the package docs about it: Compatibility.

Weak dependencies


This is a somewhat advanced usage of Pkg which can be skipped for people new to Julia and Julia packages.


The described feature requires Julia 1.9+.

A weak dependency is a dependency that will not automatically install when the package is installed but you can still control what versions of that package are allowed to be installed by setting compatibility on it. These are listed in the project file under the [weakdeps] section:

SomePackage = "b3785f31-9d33-4cdf-bc73-f646780f1739"

SomePackage = "1.2"

The current usage of this is almost solely limited to "extensions" which is described in the next section.

Conditional loading of code in packages (Extensions)


This is a somewhat advanced usage of Pkg which can be skipped for people new to Julia and Julia packages.


The described feature requires Julia 1.9+.

Sometimes one wants to make two or more packages work well together, but may be reluctant (perhaps due to increased load times) to make one an unconditional dependency of the other. A package extension is a module in a file (similar to a package) that is automatically loaded when some other set of packages are loaded into the Julia session. This is very similar to functionality that the external package Requires.jl provides, but which is now available directly through Julia, and provides added benefits such as being able to precompile the extension.

Code structure

A useful application of extensions could be for a plotting package that should be able to plot objects from a wide variety of different Julia packages. Adding all those different Julia packages as dependencies of the plotting package could be expensive since they would end up getting loaded even if they were never used. Instead, the code required to plot objects for specific packages can be put into separate files (extensions) and these are loaded only when the packages that define the type(s) we want to plot are loaded.

Below is an example of how the code can be structured for a use case in which a Plotting package wants to be able to display objects defined in the external package Contour. The file and folder structure shown below is found in the Plotting package.


name = "Plotting"
version = "0.1.0"
uuid = "..."

Contour = "d38c429a-6771-53c6-b99e-75d170b6e991"

# name of extension to the left
# extension dependencies required to load the extension to the right
# use a list for multiple extension dependencies
PlottingContourExt = "Contour"

Contour = "0.6.2"


module Plotting

function plot(x::Vector)
    # Some functionality for plotting a vector here

end # module

ext/PlottingContourExt.jl (can also be in ext/PlottingContourExt/PlottingContourExt.jl):

module PlottingContourExt # Should be same name as the file (just like a normal package)

using Plotting, Contour

function Plotting.plot(c::Contour.ContourCollection)
    # Some functionality for plotting a contour here

end # module

Extensions can have any arbitrary name (here PlottingContourExt), but using something similar to the format of this example that makes the extended functionality and dependency of the extension clear is likely a good idea.


Often you will put the extension dependencies into the test target so they are loaded when running e.g. Pkg.test(). On earlier Julia versions this requires you to also put the package in the [extras] section. This is unfortunate but the project verifier on older Julia versions will complain if this is not done.


If you use a manifest generated by a Julia version that does not know about extensions with a Julia version that does know about them, the extensions will not load. This is because the manifest lacks some information that tells Julia when it should load these packages. So make sure you use a manifest generated at least the Julia version you are using.

Behavior of extensions

A user that depends only on Plotting will not pay the cost of the "extension" inside the PlottingContourExt module. It is only when the Contour package actually gets loaded that the PlottingContourExt extension is loaded too and provides the new functionality.

In our example, the new functionality is an additional method, which we add to an existing function from the parent package Plotting. Implementing such methods is among the most standard use cases of package extensions. Within the parent package, the function to extend can even be defined with zero methods, as follows:

function plot end

If one considers PlottingContourExt as a completely separate package, it could be argued that defining Plotting.plot(c::Contour.ContourCollection) is type piracy since PlottingContourExt owns neither the function Plotting.plot nor the type Contour.ContourCollection. However, for extensions, it is ok to assume that the extension owns the functions in its parent package.

In other situations, one may need to define new symbols in the extension (types, structs, functions, etc.) instead of reusing those from the parent package. Such symbols are created in a separate module corresponding to the extension, namely PlottingContourExt, and thus not in Plotting itself. If extension symbols are needed in the parent package, one must call Base.get_extension to retrieve them. Here is an example showing how a custom type defined in PlottingContourExt can be accessed in Plotting:

ext = Base.get_extension(@__MODULE__, :PlottingContourExt)
if !isnothing(ext)
    ContourPlotType = ext.ContourPlotType

On the other hand, accessing extension symbols from a third-party package (i.e. not the parent) is not a recommended practice at the moment.

Backwards compatibility

This section discusses various methods for using extensions on Julia versions that support them, while simultaneously providing similar functionality on older Julia versions.


This section is relevant if you are currently using Requires.jl but want to transition to using extensions (while still having Requires be used on Julia versions that do not support extensions). This is done by making the following changes (using the example above):

  • Add the following to the package file. This makes it so that Requires.jl loads and inserts the callback only when extensions are not supported

    # This symbol is only defined on Julia versions that support extensions
    if !isdefined(Base, :get_extension)
    using Requires
    @static if !isdefined(Base, :get_extension)
    function __init__()
        @require Contour = "d38c429a-6771-53c6-b99e-75d170b6e991" include("../ext/PlottingContourExt.jl")

    or if you have other things in your __init__() function:

    if !isdefined(Base, :get_extension)
    using Requires
    function __init__()
        # Other init functionality here
        @static if !isdefined(Base, :get_extension)
            @require Contour = "d38c429a-6771-53c6-b99e-75d170b6e991" include("../ext/PlottingContourExt.jl")
  • Make the following change in the conditionally-loaded code:

    isdefined(Base, :get_extension) ? (using Contour) : (using ..Contour)
  • Add Requires to [weakdeps] in your Project.toml file, so that it is listed in both [deps] and [weakdeps]. Julia 1.9+ knows to not install it as a regular dependency, whereas earlier versions will consider it a dependency.

The package should now work with Requires.jl on Julia versions before extensions were introduced and with extensions on more recent Julia versions.

Transition from normal dependency to extension

This section is relevant if you have a normal dependency that you want to transition be an extension (while still having the dependency be a normal dependency on Julia versions that do not support extensions). This is done by making the following changes (using the example above):

  • Make sure that the package is both in the [deps] and [weakdeps] section. Newer Julia versions will ignore dependencies in [deps] that are also in [weakdeps].
  • Add the following to your main package file (typically at the bottom):
    if !isdefined(Base, :get_extension)

Using an extension while supporting older Julia versions

In the case where one wants to use an extension (without worrying about the feature of the extension being available on older Julia versions) while still supporting older Julia versions the packages under [weakdeps] should be duplicated into [extras]. This is an unfortunate duplication, but without doing this the project verifier under older Julia versions will throw an error if it finds packages under [compat] that is not listed in [extras].

Package naming guidelines

Package names should be sensible to most Julia users, even to those who are not domain experts. The following guidelines apply to the General registry but may be useful for other package registries as well.

Since the General registry belongs to the entire community, people may have opinions about your package name when you publish it, especially if it's ambiguous or can be confused with something other than what it is. Usually, you will then get suggestions for a new name that may fit your package better.

  1. Avoid jargon. In particular, avoid acronyms unless there is minimal possibility of confusion.

    • It's ok to say USA if you're talking about the USA.
    • It's not ok to say PMA, even if you're talking about positive mental attitude.
  2. Avoid using Julia in your package name or prefixing it with Ju.

    • It is usually clear from context and to your users that the package is a Julia package.
    • Package names already have a .jl extension, which communicates to users that Package.jl is a Julia package.
    • Having Julia in the name can imply that the package is connected to, or endorsed by, contributors to the Julia language itself.
  3. Packages that provide most of their functionality in association with a new type should have pluralized names.

    • DataFrames provides the DataFrame type.
    • BloomFilters provides the BloomFilter type.
    • In contrast, JuliaParser provides no new type, but instead new functionality in the JuliaParser.parse() function.
  4. Err on the side of clarity, even if clarity seems long-winded to you.

    • RandomMatrices is a less ambiguous name than RndMat or RMT, even though the latter are shorter.
  5. A less systematic name may suit a package that implements one of several possible approaches to its domain.

    • Julia does not have a single comprehensive plotting package. Instead, Gadfly, PyPlot, Winston and other packages each implement a unique approach based on a particular design philosophy.
    • In contrast, SortingAlgorithms provides a consistent interface to use many well-established sorting algorithms.
  6. Packages that wrap external libraries or programs should be named after those libraries or programs.

    • CPLEX.jl wraps the CPLEX library, which can be identified easily in a web search.
    • MATLAB.jl provides an interface to call the MATLAB engine from within Julia.
  7. Avoid naming a package closely to an existing package

    • Websocket is too close to WebSockets and can be confusing to users. Rather use a new name such as SimpleWebsockets.

Registering packages

Once a package is ready it can be registered with the General Registry (see also the FAQ). Currently, packages are submitted via Registrator. In addition to Registrator, TagBot helps manage the process of tagging releases.

Best Practices

Packages should avoid mutating their own state (writing to files within their package directory). Packages should, in general, not assume that they are located in a writable location (e.g. if installed as part of a system-wide depot) or even a stable one (e.g. if they are bundled into a system image by PackageCompiler.jl). To support the various use cases in the Julia package ecosystem, the Pkg developers have created a number of auxiliary packages and techniques to help package authors create self-contained, immutable, and relocatable packages:

  • Artifacts can be used to bundle chunks of data alongside your package, or even allow them to be downloaded on-demand. Prefer artifacts over attempting to open a file via a path such as joinpath(@__DIR__, "data", "my_dataset.csv") as this is non-relocatable. Once your package has been precompiled, the result of @__DIR__ will have been baked into your precompiled package data, and if you attempt to distribute this package, it will attempt to load files at the wrong location. Artifacts can be bundled and accessed easily using the artifact"name" string macro.

  • Scratch.jl provides the notion of "scratch spaces", mutable containers of data for packages. Scratch spaces are designed for data caches that are completely managed by a package and should be removed when the package itself is uninstalled. For important user-generated data, packages should continue to write out to a user-specified path that is not managed by Julia or Pkg.

  • Preferences.jl allows packages to read and write preferences to the top-level Project.toml. These preferences can be read at runtime or compile-time, to enable or disable different aspects of package behavior. Packages previously would write out files to their own package directories to record options set by the user or environment, but this is highly discouraged now that Preferences is available.