Open Source Julia Software
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Julia Software
Browse free open source Julia Software and projects below. Use the toggles on the left to filter open source Julia Software by OS, license, language, programming language, and project status.
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Genie.jl
The highly productive Julia web framework
Genie Framework includes all you need to quickly build production-ready web applications with Julia. Develop Julia backends, create beautiful web UIs, build data applications and dashboards, integrate with databases and set up high-performance web services and APIs. Genie Builder is a free VSCode plugin for quickly building Julia apps without writing frontend code. Drag and drop UI components such as text, sliders, plots, and data tables onto a canvas, and connect them to the variables in the backend code. Genie.jl is the backbone of Genie Framework: the complete solution for developing modern full-stack web applications in Julia. Genie.jl includes key features like the webserver, the flexible templating engine with support for HTML, JSON, Markdown, and Julia views, caching, (encrypted) cookies and sessions, forms handling, and the powerful router. Genie.jl uses the familiar MVC architecture, follows industry best practices, and comes with lots of useful code generators. -
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JuMP
Modeling language for Mathematical Optimization
JuMP is a modeling language and collection of supporting packages for mathematical optimization in Julia. JuMP makes it easy to formulate and solve a range of problem classes, including linear programs, integer programs, conic programs, semidefinite programs, and constrained nonlinear programs. JuMP is used to solve large-scale inventory routing problems at Renault, schedule trains at Thales Inc., plan power grid expansion at PSR, and route school buses. -
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
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AMDGPU.jl
AMD GPU (ROCm) programming in Julia
AMD GPU (ROCm) programming in Julia. -
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Evolutionary.jl
Evolutionary & genetic algorithms for Julia
A Julia package for evolutionary & genetic algorithms. The package can be installed with the Julia package manager. -
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MethodOfLines.jl
Automatic Finite Difference PDE solving with Julia SciML
MethodOfLines.jl is a Julia package for automated finite difference discretization of symbolically defined PDEs in N dimensions. It uses symbolic expressions for systems of partial differential equations as defined with ModelingToolkit.jl, and Interval from DomainSets.jl to define the space(time) over which the simulation runs. This project is under active development, therefore the interface is subject to change. The docs will be updated to reflect any changes, please check back for current usage information. -
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NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers
Fast implementations of root-finding algorithms in Julia that satisfy the SciML common interface. For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation that contains the unreleased features. NonlinearSolve.jl is a unified interface for the nonlinear solving packages of Julia. The package includes its own high-performance nonlinear solvers which include the ability to swap out to fast direct and iterative linear solvers, along with the ability to use sparse automatic differentiation for Jacobian construction and Jacobian-vector products. NonlinearSolve.jl interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. -
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TaylorSeries.jl
Taylor polynomial expansions in one and several independent variables
A Julia package for Taylor polynomial expansions in one or more independent variables. -
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ChatGPT Plugins Collection
An unofficial collection of Plugins for ChatGPT
ChatGPT-Plugins-Collection is a community-driven repository that gathers examples and resources for building, testing, and experimenting with ChatGPT plugins. The collection provides a variety of plugin implementations that showcase different use cases, helping developers learn how to extend ChatGPT’s functionality. It is designed to serve both as a learning resource for beginners and a reference point for more experienced developers. By centralizing community contributions, the repository highlights practical applications of plugins across domains such as productivity, data access, and automation. The project also serves as a starting point for developers interested in building their own custom plugins, offering inspiration and code samples. With its open structure, it encourages collaboration and knowledge sharing in the growing ecosystem of ChatGPT extensions. -
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HTTP.jl
HTTP for Julia
HTTP.jl is a pure Julia implementation of the HTTP protocol, providing tools for building HTTP clients and servers. It enables users to send requests, handle responses, and construct REST APIs or web services entirely in Julia. HTTP.jl supports TLS, cookies, headers, streaming, and middleware, making it suitable for both simple scripting and full-scale web service development. -
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Krylov.jl
A Julia Basket of Hand-Picked Krylov Methods
If you use Krylov.jl in your work, please cite it using the metadata given in CITATION.cff. -
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PythonCall & JuliaCall
Python and Julia in harmony
Bringing Python® and Julia together in seamless harmony. Call Python code from Julia and Julia code from Python via a symmetric interface. Simple syntax, so the Python code looks like Python and the Julia code looks like Julia. Intuitive and flexible conversions between Julia and Python: anything can be converted, you are in control. Fast non-copying conversion of numeric arrays in either direction: modify Python arrays (e.g. bytes, array. array, numpy.ndarray) from Julia or Julia arrays from Python. Helpful wrappers: interpret Python sequences, dictionaries, arrays, dataframes and IO streams as their Julia counterparts, and vice versa. -
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SymbolicUtils.jl
Symbolic expressions, rewriting and simplification
SymbolicUtils is a practical symbolic programming utility in Julia. It lets you create, rewrite and simplify symbolic expressions, and generate Julia code from them. SymbolicUtils.jl provides various utilities for symbolic computing. SymbolicUtils.jl is what one would use to build a Computer Algebra System (CAS). If you're looking for a complete CAS, similar to SymPy or Mathematica, see Symbolics.jl. If you want to build a crazy CAS for your weird Octonian algebras, you've come to the right place. Symbols in SymbolicUtils carry type information. Operations on them propagate this information. A rule-based rewriting language can be used to find subexpressions that satisfy arbitrary conditions and apply arbitrary transformations on the matches. The library also contains a set of useful simplification rules for expressions of numeric symbols and numbers. These can be remixed and extended for special purposes. -
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TaylorIntegration.jl
ODE integration using Taylor's method, and more, in Julia
ODE integration using Taylor's method in Julia. -
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Finch.jl
Sparse tensors in Julia and more
Finch is a cutting-edge Julia-to-Julia compiler specially designed for optimizing loop nests over sparse or structured multidimensional arrays. Finch empowers users to write conventional for loops which are transformed behind-the-scenes into fast sparse code. -
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Trixi.jl
Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs
Trixi.jl is a numerical simulation framework for hyperbolic conservation laws written in Julia. A key objective for the framework is to be useful to both scientists and students. Therefore, next to having an extensible design with a fast implementation, Trixi.jl is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures. -
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WaterLily.jl
Fast and simple fluid simulator in Julia
WaterLily.jl is a fluid dynamics simulation package in Julia that uses lattice Boltzmann methods (LBM) to simulate incompressible flows and fluid-structure interactions in two dimensions. It is designed for easy use and rapid prototyping of physical systems involving particles, obstacles, and hydrodynamic forces. The package is well-suited for educational purposes, computational physics research, and lightweight fluid simulations. -
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FourierFlows.jl
Tools for building fast, hackable, pseudospectral equation solvers
This software provides tools for partial differential equations on periodic domains using Fourier-based pseudospectral methods. A central intent of the software's design is also to provide a framework for writing new, fast solvers for new physical problems. The code is written in Julia. -
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AbstractGPs.jl
Abstract types and methods for Gaussian Processes
AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs. You may want to go through the main API design documentation. -
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CondaPkg.jl
Add Conda dependencies to your Julia project
Add Conda dependencies to your Julia project. This package is a lot like Pkg from the Julia standard library, except that it is for managing Conda packages. Conda dependencies are defined in CondaPkg.toml, which is analogous to Project.toml. CondaPkg will install these dependencies into a Conda environment specific to the current Julia project. Hence dependencies are isolated from other projects or environments. Functions like add, rm, status exist to edit the dependencies programmatically. Or you can do pkg> conda add some_package to edit the dependencies from the Pkg REPL. -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
ImplicitGlobalGrid is an outcome of a collaboration of the Swiss National Supercomputing Centre, ETH Zurich (Dr. Samuel Omlin) with Stanford University (Dr. Ludovic Räss) and the Swiss Geocomputing Centre (Prof. Yuri Podladchikov). It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the Julia MPI wrapper (MPI.jl) to perform halo updates close to hardware limit and leverages CUDA-aware or ROCm-aware MPI for GPU-applications. The communication can straightforwardly be hidden behind computation [1, 3] (how this can be done automatically when using ParallelStencil.jl is shown in; a general approach particularly suited for CUDA C applications is explained in. -
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Julia VS Code
Julia extension for Visual Studio Code
This VS Code extension provides support for the Julia programming language. We build on Julia’s unique combination of ease-of-use and performance. Beginners and experts can build better software more quickly, and get to a result faster. With a completely live environment, Julia for VS Code aims to take the frustration and guesswork out of programming and put the fun back in. A hybrid “canvas programming” style combines the exploratory power of a notebook with the productivity and static analysis features of an IDE. VS Code is a powerful editor and customizable to your heart’s content (though the defaults are pretty good too). It has power features like multiple cursors, fuzzy file finding and Vim keybindings. -
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Penumbra
Penumbra Color Theme
Penumbra is a mathematically balanced color scheme designed in a perceptually uniform color space, with base colors inspired by the natural interplay of sunlight and sky. It separates luminance, chroma, and hue to make the most efficient use of the available color space on standard electronic displays. The palette consists of nine nearly symmetric base colors, which are used to build the main light and dark themes, along with two additional high-contrast dark variants tailored for people with mild to moderate visual impairments. Its design focuses on functionality first, while maintaining an aesthetic quality that draws from familiar natural tones. Beyond its use in text editors and terminal environments, Penumbra’s carefully structured accent palettes are also suited for encoding information in data visualizations, where perceptual uniformity and hue differentiability are critical. -
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Agents.jl
Agent-based modeling framework in Julia
Agents.jl is a pure Julia framework for agent-based modeling (ABM): a computational simulation methodology where autonomous agents react to their environment (including other agents) given a predefined set of rules. The simplicity of Agents.jl is due to the intuitive space-agnostic modeling approach we have implemented: agent actions are specified using generically named functions (such as "move agent" or "find nearby agents") that do not depend on the actual space the agents exist in, nor on the properties of the agents themselves. Overall this leads to ultra-fast model prototyping where even changing the space the agents live in is a matter of only a couple of lines of code. -
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BlockArrays.jl
BlockArrays for Julia
A block array is a partition of an array into blocks or subarrays, see Wikipedia for a more extensive description. This package has two purposes. Firstly, it defines an interface for an AbstractBlockArray block arrays that can be shared among types representing different types of block arrays. The advantage to this is that it provides a consistent API for block arrays. Secondly, it also implements two different types of block arrays that follow the AbstractBlockArray interface. The type BlockArray stores each block contiguously while the type PseudoBlockArray stores the full matrix contiguously. This means that BlockArray supports fast noncopying extraction and insertion of blocks while PseudoBlockArray supports fast access to the full matrix to use in for example a linear solver.
