Welcoming Repositories Index Julia Ecosystem
Top Welcoming Open-Source Julia Repositories
Top welcoming Julia repositories. Scored by real 90-day external PR merge rates, maintainer review turnaround speed, and first-time contributor success.
Updated daily • Sun, 11 Oct 2026 UTC
Tracked Repos
908
>100 stars verified
Avg Merge Rate
78%
90-day external PRs
Open GFIs
19
Beginner friendly issues
Welcoming Tier
50
S & A Tier maintainers
Ranked Julia Repositories
Showing top 50 of 908 ranked repositories
| Rank | Repository | Tier | Score | Merge Rate | Good First Issues |
|---|---|---|---|---|---|
| #1 | Computational geometry in Julia | S•Elite | 74.2 | 69.2% | 2 |
| #2 | Hypothesis tests for Julia | S•Elite | 72.5 | 81.8% | - |
| #3 | Symbolic programming for the next generation of numerical software | S•Elite | 72.3 | 75.4% | - |
| #4 | The official registry of general Julia packages | S•Elite | 71.2 | 89.0% | - |
| #5 | Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R | S•Elite | 71.0 | 74.9% | - |
| #6 | Interpreter for Julia code | S•Elite | 70.4 | 87.2% | - |
| #7 | CPU/GPU portable array, parallel_for/parallel_reduce in Julia for productive science. Funded by the US DOE Advanced Scientific Computing Research (ASCR). | A•Welcoming | 70.0 | 76.2% | - |
| #8 | Generic Mapping Tools Library Wrapper for Julia | A•Welcoming | 70.0 | 100.0% | - |
| #9 | An interface to various automatic differentiation backends in Julia. | A•Welcoming | 69.1 | 89.3% | - |
| #10 | Finite element toolbox for Julia | A•Welcoming | 68.8 | 69.6% | 8 |
| #11 | Julia debugger | A•Welcoming | 68.8 | 86.7% | - |
| #12 | Fast and automatic structural identifiability software for ODE systems | A•Welcoming | 68.7 | 85.7% | - |
| #13 | An implementation of the Microsoft Language Server Protocol for the Julia language. | A•Welcoming | 68.5 | 82.8% | - |
| #14 | An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations | A•Welcoming | 68.5 | 81.6% | - |
| #15 | A data structure for mathematical optimization problems | A•Welcoming | 68.3 | 76.8% | - |
| #16 | Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods | A•Welcoming | 67.8 | 87.8% | - |
| #17 | An opinionated code formatter for Julia. Plot twist - the opinion is your own. | A•Welcoming | 66.7 | 90.3% | 4 |
| #18 | Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more. | A•Welcoming | 66.7 | 59.0% | 1 |
| #19 | Reservoir computing utilities for scientific machine learning (SciML) | A•Welcoming | 66.6 | 81.5% | - |
| #20 | Neural Network primitives with multiple backends | A•Welcoming | 66.4 | 85.2% | - |
| #21 | LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface. | A•Welcoming | 65.8 | 78.8% | - |
| #22 | High-performance reactive message-passing based Bayesian inference engine | A•Welcoming | 65.6 | 93.8% | - |
| #23 | Fast and simple fluid simulator in Julia | A•Welcoming | 65.4 | 71.1% | - |
| #24 | Implementation of a language-level autograd compiler | A•Welcoming | 65.2 | 72.7% | - |
| #25 | Computational algebraic number theory | A•Welcoming | 65.1 | 79.8% | - |
| #26 | Quantum Toolbox in Julia | A•Welcoming | 65.0 | 83.3% | - |
| #27 | Julia package for tensor contractions and related operations | A•Welcoming | 65.0 | 86.2% | - |
| #28 | Reusable array functionality for Julia's various GPU backends. | A•Welcoming | 64.8 | 72.1% | - |
| #29 | Metal programming in Julia | A•Welcoming | 64.6 | 81.1% | - |
| #30 | jump-dev/JuMP.jl2,482 Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear) | A•Welcoming | 64.5 | 87.7% | - |
| #31 | A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc. | A•Welcoming | 64.5 | 77.5% | - |
| #32 | Random Integrators for many-body quantum systems | A•Welcoming | 64.4 | 77.8% | - |
| #33 | High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML) | A•Welcoming | 64.4 | 73.4% | - |
| #34 | Reusable compiler infrastructure for Julia GPU backends. | A•Welcoming | 64.0 | 78.4% | - |
| #35 | JuliaLang/julia49,203 The Julia Programming Language | A•Welcoming | 63.7 | 60.6% | 1 |
| #36 | Symbolic expressions, rewriting and simplification | A•Welcoming | 63.6 | 80.6% | - |
| #37 | A Julia package dedicated to simulating quantum many-body systems using Matrix Product States (MPS) | A•Welcoming | 63.6 | 72.1% | - |
| #38 | The Base interface of the SciML ecosystem | A•Welcoming | 63.5 | 79.2% | - |
| #39 | Heterogeneous programming in Julia | A•Welcoming | 63.3 | 62.0% | - |
| #40 | Julia library for stylized terminal output | A•Welcoming | 63.1 | 72.7% | 1 |
| #41 | CLI app for compiling and bundling julia binaries, especially trimmed ones | A•Welcoming | 63.1 | 70.4% | - |
| #42 | FluxML/Flux.jl4,753 Relax! Flux is the ML library that doesn't make you tensor | A•Welcoming | 63.0 | 80.0% | - |
| #43 | Julia bindings for the Enzyme automatic differentiator | A•Welcoming | 62.9 | 67.3% | - |
| #44 | A documentation generator for Julia. | A•Welcoming | 62.9 | 54.5% | 1 |
| #45 | 🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs | A•Welcoming | 62.7 | 65.2% | - |
| #46 | Template for Julia Programming Language packages using the copier engine. | A•Welcoming | 62.4 | 96.6% | 1 |
| #47 | Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface. | A•Welcoming | 62.3 | 75.2% | - |
| #48 | An atmospheric model for research: friendly, interactive, extensible, and built for speed | A•Welcoming | 62.2 | 78.7% | - |
| #49 | Utilities and abstractions for Machine Learning tasks | A•Welcoming | 62.0 | 95.7% | - |
| #50 | GPU-capable global atmosphere model of the CliMA Earth System Model, designed for calibration with data assimilation and machine learning | A•Welcoming | 62.0 | 68.5% | - |
How to Get Your Julia PR Merged
1
Check C-Rank™ First: Pick projects with S or A tiers to avoid maintainer ghosts and slow review cycles.
2
Run Local Verification: Ensure test suites and formatting linters pass before opening a pull request.
3
Submit in Maintainer Windows: Check the repository report card to submit when maintainers are most active.
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Frequently Asked Questions: Julia Open Source
What is the most welcoming Julia open-source repository?
The highest-ranked Julia repository on GetMerged is currently JuliaGeometry/Meshes.jl, with a C-Rank score of 74.2/100 and a 69.2% PR merge rate.
How can I find good first issues in Julia?
You can browse verified beginner-friendly issues in Julia by visiting our curated Good First Issues directory at getmerged.abhishekco.de/good-first-issue/julia.
What makes a Julia repository ‘Super Welcoming’ on GetMerged?
A ‘Super Welcoming’ (S-Tier) Julia repository demonstrates an external PR merge rate above 80%, a median first response time within 24 to 48 hours, active review feedback, and clear onboarding documentation.