Crate jlrs

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jlrs is a crate that provides access to most of the Julia C API, it can be used to embed Julia in Rust applications and to use functionality from the Julia C API when writing ccallable functions in Rust. Currently this crate is only tested on Linux and Windows in combination with Julia 1.6, 1.7, and 1.8.0-rc3 and is not compatible with other versions of Julia.

The documentation assumes you’re already familiar with the Julia programming language.

An incomplete list of features that are currently supported by jlrs:

  • Access arbitrary Julia modules and their contents.
  • Call Julia functions, including functions that take keyword arguments.
  • Handle exceptions or convert them to an error message, optionally with color.
  • Include and call your own Julia code.
  • Use a custom system image.
  • Create values that Julia can use, and convert them back to Rust, from Rust.
  • Access the type information and fields of values. The contents of inline and bits-union fields can be accessed directly.
  • Create and use n-dimensional arrays. The jlrs-ndarray feature can be enabled for integration with ndarray.
  • Support for mapping Julia structs to Rust structs that can be generated by JlrsReflect.jl.
  • Structs that can be mapped to Rust include those with type parameters and bits unions.
  • An async runtime is available which can be used from multiple threads and supports scheduling Julia Tasks and awaiting the result without blocking the runtime thread.

NB: Active development happens on the dev branch, the master branch points to the most recently released version.


Julia must be installed before jlrs can be used. Only version 1.6, 1.7, and 1.8.0-rc3 are supported. Using version 1.6 requires enabling the lts feature, 1.8.0-rc3 requires enabling the rc3 feature.


The recommended way to install Julia is to download the binaries from the official website, which is distributed in an archive containing a directory called julia-x.y.z. This directory contains several other directories, including a bin directory containing the julia executable.

During compilation, the paths to the header and library are normally detected automatically by executing the command which julia. The path to julia.h must be $(which julia)/../include/julia/julia.h and the path to the library $(which julia)/../lib/ If you want to override this default behaviour the JULIA_DIR environment variable must be set to the path to the appropriate julia.x-y-z directory; in this case $JULIA_DIR/include/julia/julia.h and $JULIA_DIR/lib/ are used instead.

In order to be able to load this file must be on the library search path. If this is not the case you must add /path/to/julia-x.y.z/lib to the LD_LIBRARY_PATH environment variable. When the uv feature is enabled, /path/to/julia-x.y.z/lib/julia must also be added to LD_LIBRARY_PATH. The latter path should not be added to the default path because this can break tools currently installed on your system.


Julia can be installed using juliaup, or with the installer or portable installation downloaded from the official website. In the first case, Julia has been likely placed in %USERPROFILE%\.julia\juliaup\julia-x.y.z+0~x64, while installing or extracting allows you to pick the destination. After installation or extraction a folder called Julia-x.y.z exists, which contains several folders including a bin folder containing julia.exe. The path to the bin folder must be added to the Path environment variable.

Julia is automatically detected by executing the command where julia. If this returns multiple locations the first one is used. The default can be overridden by setting the JULIA_DIR environment variable. This doesn’t work correctly with juliaup, in this case the environment variable must be set.

Note that while Julia 1.6 is supported on Windows, several methods are currently unavailable when this version is used.

If you use the MSVC target, you must create two or three lib files using lib.exe. The def files required for this can be found in the def folder in the jl-sys crate. To create the lib files, copy the three files from either the lts, stable, or rc3 folder to the bin folder where Julia is installed. Afterwards, open a Developer Command Prompt for VS19 and execute the following commands:

cd C:\Path\To\Julia-x.y.z\bin
lib /def:libjulia.def /out:libjulia.lib /machine:x64
lib /def:libopenlibm.def /out:libopenlibm.lib /machine:x64
lib /def:libuv-2.def /out:libuv-2.lib /machine:x64

The final command only needs to be executed if the uv feature has been enabled. If you use the GNU target these lib files must not exist.


Most functionality of jlrs is only available if the proper features are enabled. These features generally belong to one of two categories: runtimes and utilities.

A runtime lets you call Julia from Rust, you must enable one of them if you want to embed Julia in a Rust application. The following features enable a runtime:

  • sync-rt Enables the sync runtime, Julia. The sync runtime provides single-threaded, blocking access to the Julia C API.

  • async-rt Enables the async runtime, AsyncJulia. The async runtime runs on a separate thread and can be used from multiple threads. While access to the C API is single-threaded, the async runtime can run multiple tasks in parallel by making use of Julia’s task system and Rust’s async/await syntax. To use this feature you must provide a backing runtime.

  • tokio-rt and async-std-rt These features provide a backing runtime for the async runtime. The first uses tokio, the second async-std. The async-rt feature is automatically enabled when one of these features is enabled.

If you’re writing a library, either one that will be called from Julia or one that will be used by a Rust application that embeds Julia, no runtime is required.

In addition to these runtimes, the following utility features are available:

  • prelude Provide a prelude module, jlrs::prelude. This feature is enabled by default.

  • lts

    Use the current LTS version of Julia (1.6) instead of the current stable version (1.7).

  • rc3

    Use the current 1.8.0-rc3 version of Julia instead of the current stable version (1.7).

  • async

    Enable the features of the async runtime which don’t depend on the backing runtime. This can be used in libraries which provide implementations of tasks that the async runtime can handle.

  • jlrs-derive

    This features should be used in combination with the JlrsReflect.jl package. This package generates Rust bindings for Julia structs, these bindings use the custom derive macros to enable the safe conversion of data from Julia to Rust, and from Rust to Julia in some cases.

  • jlrs-ndarray

    Access the contents of a Julia array as an ArrayView or ArrayViewMut from ndarray.

  • f16

    Adds support for working with Julia’s Float16 type from Rust using half’s f16 type.

  • ccall

    Julia’s ccall interface can be used to call functions written in Rust from Julia. No runtime can be used in this case because Julia has already been initialized, when this feature is enabled the CCall struct is available which offers the same functionality as the sync runtime without initializing Julia.

  • uv

    This feature enables the method CCall::uv_async_send, which can be used to wake a Julia AsyncCondition from Rust. The ccall feature is automically enabled when this feature is used.

  • pyplot

    This feature lets you plot data using the Pyplot package and Gtk 3 from Rust.

  • i686

    Link with a 32-bit build of Julia.

  • debug

    Link with a debug build of Julia on Linux.

You can enable all features except lts and debug by enabling the full feature.

Using this crate

If you want to embed Julia and call it from Rust, you must enable a runtime feature:

jlrs = {version = "0.15", features = ["sync-rt"]}

jlrs = {version = "0.15", features = ["tokio-rt"]}

jlrs = {version = "0.15", features = ["async-std-rt"]}

When Julia is embedded in an application, it must be initialized before it can be used. The following snippet initializes the sync runtime:

use jlrs::prelude::*;

// Initializing Julia is unsafe because this can load arbitrary
// Julia code, and because it can race with other crates unrelated
// to jlrs. It returns an error if Julia has already been
// initialized.
let _julia = unsafe { RuntimeBuilder::new().start().unwrap() };

To use the async runtime you must upgrade the RuntimeBuilder to an AsyncRuntimeBuilder by providing a backing runtime and channel. Implementations for tokio and async-std are available if these features have been enabled. For example, an async runtime backed by tokio and an unbounded channel can be initialized as follows if the tokio-rt feature is enabled:

use jlrs::prelude::*;

// Initializing Julia is unsafe for the same reasons as the sync runtime.
let (_julia, _task_handle) = unsafe {
        .async_runtime::<Tokio, UnboundedChannel<_>>()

The async runtime can also be started asynchronously:

use jlrs::prelude::*;

async fn main() {
    // Initializing Julia is unsafe for the same reasons as the sync runtime.
    let (_julia, _task_handle) = unsafe {
            .async_runtime::<Tokio, UnboundedChannel<_>>()

If you’re calling Rust from Julia everything has already been initialized. If the ccall feature is enabled CCall is available which provides the same functionality as the sync runtime.

Calling Julia from Rust

This section will focus on some topics that are common between the sync and async runtimes.

After initialization you have an instance of Julia or AsyncJulia, both provide a method called include that lets you include files with custom Julia code. In order to create Julia data and call Julia functions, a scope must be created.

When the sync runtime is used this can be done by calling the methods Julia::scope and Julia::scope_with_capacity. These methods take a closure with two arguments, a Global and a GcFrame (frame). The first is an access token for global Julia data, the second is used to root non-global data. While non-global data is rooted, it won’t be freed by Julia’s garbage collector. The frame is created when Julia::scope(_with_capacity) is called and dropped when it returns, so any data rooted in the frame associated with a scope won’t be freed by the garbage collector until leaving that scope.

The async runtime can’t create a new scope directly, AsyncJulia is a handle to the async runtime which runs on another thread. Instead, the async runtime deals with tasks. The simplest of these is a blocking task, which can be executed by calling AsyncJulia::(try_)blocking_task(_with_capacity). These methods accept any closure Julia::scope can handle with the additional requirement that it must be Send and Sync. It’s called a blocking task because the runtime is blocked while executing this task. The other kinds of tasks that the async runtime can handle will be introduced later.

Inside the closure provided to Julia::scope or AsyncJulia::blocking_task it’s possible to interact with Julia. Global Julia data can be accessed through its module system, the methods Module::main, Module::base, and Module::core can be used to access the Main, Base, and Core modules respectively. The contents of these modules can then be accessed by calling Module::function which returns a Function, Module::global which returns a Value, and Module::submodule which returns another Module.

Value provides several methods to allocate new Julia data. The simplest one is Value::eval_string, which evaluates the contents of the string passed to it and returns the result as a Value. For example, you can evaluate 2 to convert it to Value. In practice, this method should rarely be used. It can be used to evaluate simple function calls like sqrt(2), but it must be parsed, compiled, and can’t take any arguments. Its most important use-case is importing installed and standard library packages by evaluating an import or using statement.

A more interesting method, Value::new, can be used with data of any type that implements IntoJulia. This trait is implemented by primitive types like i8 and char. Any type that implements IntoJulia also implements Unbox which is used to extract the contents of a Value. Because sqrt(2) returns a Float64, it can be unboxed as an f64. Pointer wrapper types don’t implement IntoJulia or Unbox, if they can be created from Rust they provide methods to do so.

It’s possible to call anything that implements Call as a Julia function. Functions can be called with any number of positional arguments and can be provided with keyword arguments. Keywords must be provided as a NamedTuple, which can be created with the named_tuple macro.

Evaluating raw code and calling Julia functions is always unsafe. Nothing prevents you from calling a function like nasaldemons() = unsafe_load(Ptr{Float64}(0x05391A445)). Similarly, mutating Julia data is unsafe because nothing prevents you from mutating data that shouldn’t be mutated, e.g. the contents of the Core module. A full overview of the rules that you should keep in mind can be found in the safety module.

As a simple example, let’s convert two numbers to Julia values and add them:

use jlrs::prelude::*;

// Initializing Julia is unsafe because it can race with another crate that does
// the same.
let mut julia = unsafe { RuntimeBuilder::new().start().unwrap() };

let res = julia.scope(|global, mut frame| {
    // Create the two arguments.
    let i = Value::new(&mut frame, 2u64)?;
    let j = Value::new(&mut frame, 1u32)?;

    // The `+` function can be found in the base module.
    let func = Module::base(global).function(&mut frame, "+")?;

    // Call the function and unbox the result as a `u64`. The result of the function
    // call is a nested `Result`; the outer error doesn't contain to any Julia
    // data, while the inner error contains the exception if one is thrown. Here the
    // exception is converted to the outer error type by calling `into_jlrs_result`, this new
    // error contains the error message Julia would have shown.
    unsafe { func.call2(&mut frame, i, j)? }

assert_eq!(res, 3);

Async and persistent tasks

In addition to blocking tasks, the async runtime lets you execute async tasks which implement the AsyncTask trait, and persistent tasks which implement PersistentTask. Both of these traits are async traits.

An async task is similar to a blocking task, except that you must implement the async run method instead of providing a closure. This method takes a Global and a an AsyncGcFrame. This new frame type not only provides access to the same features as GcFrame, it can also be used to call async methods provided by the CallAsync trait. These methods schedule a function call as a new Julia Task and can be awaited until this task has completed. The async runtime can switch to another task while the result is pending, allowing multiple tasks to progress.

The previous example can be rewritten as an async task:

use jlrs::prelude::*;

struct AdditionTask {
    a: u64,
    b: u32,

// Only the runtime thread can call the Julia C API, so the async
// trait methods of `AsyncTask` must not return a future that
// implements `Send` or `Sync`.
impl AsyncTask for AdditionTask {
    // The type of the result of this task if it succeeds.
    type Output = u64;

    // This async method replaces the closure from the previous examples,
    // an `AsyncGcFrame` can be used the same way as a `GcFrame` but also
    // can be used in combination with methods from the `CallAsync` trait.
    async fn run<'frame>(
        &mut self,
        global: Global<'frame>,
        mut frame: AsyncGcFrame<'frame>,
    ) -> JlrsResult<Self::Output> {
        let a = Value::new(&mut frame, self.a)?;
        let b = Value::new(&mut frame, self.b)?;
        let func = Module::base(global).function(&mut frame, "+")?;

        // CallAsync::call_async schedules the function call on another
        // thread and returns a Future that resolves when the scheduled
        // function has returned or thrown an error.
        unsafe { func.call_async(&mut frame, &mut [a, b]) }

While blocking and async tasks run once and return their result, a persistent task returns a handle. This handle can be shared across threads and used to call its run method. In addition to a global and an async frame, this method can use the state and input data provided by the caller.

As an example, let’s accumulate some number of values in a Julia array and return the sum of its contents:

use jlrs::prelude::*;

struct AccumulatorTask {
    n_values: usize

struct AccumulatorTaskState {
    array: TypedArray<'static, 'static, usize>,
    offset: usize

// Only the runtime thread can call the Julia C API, so the async trait
// methods of `PersistentTask` must not return a future that implements
// `Send` or `Sync`.
impl PersistentTask for AccumulatorTask {
    // The type of the result of the task if it succeeds.
    type Output = usize;
    // The type of the task's internal state.
    type State = AccumulatorTaskState;
    // The type of the additional data that the task must be called with.
    type Input = usize;

    // This method is called before the task can be called. Note that the
    // lifetime of the frame is `'static`: the frame is not dropped until
    // the task has completed, so the task's internal state can contain
    // Julia data rooted in this frame.
    async fn init(
        &mut self,
        _global: Global<'static>,
        frame: &mut AsyncGcFrame<'static>,
    ) -> JlrsResult<Self::State> {
        // A `Vec` can be moved from Rust to Julia if the element type
        // implements `IntoJulia`.
        let data = vec![0usize; self.n_values];
        let array = TypedArray::from_vec(&mut *frame, data, self.n_values)?
        Ok(AccumulatorTaskState {
            offset: 0
    // Whenever the task is called through its handle this method
    // is called. Unlike `init`, the frame that this method can use
    // is dropped after `run` returns.
    async fn run<'frame>(
        &mut self,
        global: Global<'static>,
        mut frame: AsyncGcFrame<'frame>,
        state: &mut Self::State,
        input: Self::Input,
    ) -> JlrsResult<Self::Output> {
            // Array data can be directly accessed from Rust.
            // TypedArray::bits_data_mut can be used if the type
            // of the elements is concrete and immutable.
            // This is safe because this is the only active reference to
            // the array.
            let mut data = unsafe { state.array.bits_data_mut(&mut frame)? };
            data[state.offset] = input;

            state.offset += 1;
            if (state.offset == self.n_values) {
                state.offset = 0;

        // Return the sum of the contents of `state.array`.
        unsafe {
                .function(&mut frame, "sum")?
                .call1(&mut frame, state.array.as_value())?

Calling Rust from Julia

Julia’s ccall interface can be used to call extern "C" functions defined in Rust, for most use-cases you shouldn’t need jlrs. There are two major ways to use ccall, with a pointer to the function or a (:function, "library") pair.

A function can be cast to a void pointer and converted to a Value:

use jlrs::prelude::*;

// This function will be provided to Julia as a pointer, so its name can be mangled.
unsafe extern "C" fn call_me(arg: bool) -> isize {
    if arg {
    } else {

let mut julia = unsafe { RuntimeBuilder::new().start().unwrap() };
julia.scope(|global, mut frame| unsafe {
    // Cast the function to a void pointer
    let call_me_val = Value::new(&mut frame, call_me as *mut std::ffi::c_void)?;

    // Value::eval_string can be used to create new functions.
    let func = Value::eval_string(
        &mut frame,
        "myfunc(callme::Ptr{Cvoid})::Int = ccall(callme, Int, (Bool,), true)"

    // Call the function and unbox the result.
    let output = func.call1(&mut frame, call_me_val)?

    assert_eq!(output, 1);


You can also use functions defined in dylib and cdylib libraries. In order to create such a library you need to add

crate-type = ["dylib"]


crate-type = ["cdylib"]

respectively to your crate’s Cargo.toml. Use a dylib if you want to use the crate in other Rust crates, but if it’s only intended to be called through ccall a cdylib is the better choice. On Linux, such a crate will be compiled to lib<crate_name>.so.

The functions you want to use with ccall must be both extern "C" functions to ensure the C ABI is used, and annotated with #[no_mangle] to prevent name mangling. Julia can find libraries in directories that are either on the default library search path or included by setting the LD_LIBRARY_PATH environment variable on Linux. If the compiled library is not directly visible to Julia, you can open it with Libdl.dlopen and acquire function pointers with Libdl.dlsym. These pointers can be called the same way as the pointer in the previous example.

If the library is visible to Julia you can access it using the library name. If call_me is defined in a crate called foo, the following should work:

ccall((:call_me, "libfoo"), Int, (Bool,), false)

One important aspect of calling Rust from other languages in general is that panicking across an FFI boundary is undefined behaviour. If you’re not sure your code will never panic, wrap it with std::panic::catch_unwind.

Most features provided by jlrs including accessing modules, calling functions, and borrowing array data require a Global or a frame. You can access these by creating an instance of CCall first. Another method provided by CCall is CCall::uv_async_send, this method can be used to wake an Base.AsyncCondition. In particular, it can be used to write a ccallable function that does its actual work on another thread, returns early and then waiting on the async condition from Julia. The advantage of this is that the long-running function won’t block Julia. In this case you will need to use GC.@preserve to ensure Julia is aware that the use of this data is still in use after the ccall has returned.


The restriction that Julia can be initialized once must be taken into account when running tests that use jlrs. The recommended approach is to create a thread-local static RefCell:

use jlrs::prelude::*;
use std::cell::RefCell;
thread_local! {
    pub static JULIA: RefCell<Julia> = {
        let julia = RefCell::new(unsafe { RuntimeBuilder::new().start().unwrap() });

        /* include everything you need to use */


A similar approach works for the async runtime:

use jlrs::prelude::*;
use std::cell::RefCell;
thread_local! {
    pub static JULIA: RefCell<AsyncJulia<Tokio>> = {
        let julia = RefCell::new(unsafe {
                .async_runtime::<Tokio, UnboundedChannel<_>>()

        /* include everything you need to use */


Tests that use these constructs can only use one thread for testing, so you must use cargo test -- --test-threads=1, otherwise the code above will panic when a test tries to initialize Julia a second time from another thread.

If you want to run all of jlrs’s tests, this requirement must be taken into account: cargo test --all-features -- --test-threads=1. Testing with the --all-features flag only works with Julia 1.8 because this overrides the lts and debug features.

Custom types

In order to map a struct in Rust to one in Julia you can derive ValidLayout, Unbox, and Typecheck. If the struct in Julia has no type parameters and is a bits type you can also derive IntoJulia.

You normally shouldn’t need to implement these structs or traits manually. The JlrsReflect package can generate correct Rust struct and automatically derive the supported traits for types that have no atomic fields, nor any tuple or union fields with type parameters. The reason for this restriction is that the layout of such fields can be very different in a way that can’t be easily represented.

These custom types can also be used when you call Rust from Julia with ccall.


Async tasks and channels that can be used with an async runtime.

Call Julia functions.

Interact with Julia when calling Rust from Julia.

Traits for converting data.

Everything related to errors.

System and Julia version information.

Traits for checking layout compatibility and enforcing layout requirements.

Julia memory management.

Reexports structs and traits you’re likely to need.

Plot data with Plots.jl and PyPlot.jl

Embed Julia in a Rust application.


Wrapper types for Julia data


Create a new named tuple. You will need a named tuple to call functions with keyword arguments.