# Install julia with Homebrew

Fast, Dynamic Programming Language. Version 1.12.6 via Homebrew; verified from local package data.

## Install

```sh
sudo av install brew:julia
```

Additional install commands:

### macOS

- Homebrew (100%):

```sh
brew install julia
```

  Evidence: provider-native install command

## Package facts

- **Package key:** brew:julia
- **Package manager:** Homebrew
- **Version:** 1.12.6
- **Source summary:** Fast, Dynamic Programming Language
- **Homepage:** <https://julialang.org/>
- **Repository:** <https://github.com/JuliaLang/julia>
- **Generated:** 2026-08-03T00:40:33+00:00

## Executables

- julia (alias)

## Install behavior

- Bottle: not available

## Freshness

- Page generated: 2026-08-03
- Package-manager version: 1.12.6
## Project history and usage

Julia is a high-performance dynamic programming language built for technical and scientific computing without the traditional split between a productive scripting language and a separate fast implementation language. Its package-manager footprint matters because the `julia` executable is a compiler, REPL, package environment manager, and scientific-computing platform in one package.

### Project history

The Julia founders publicly announced the language on February 14, 2012 in the essay 'Why We Created Julia', after roughly two and a half years of work. The design goal was explicit: combine the usability of high-level languages with performance suitable for numerical and systems-heavy work.

Julia 1.0 was released during JuliaCon 2018, with the project describing it as the culmination of nearly a decade of work. The 1.0 milestone established the stable 1.x language line and helped shift Julia from research-language curiosity to production-usable scientific platform.

### Adoption history

Julia adoption grew through scientific computing, optimization, data science, differentiable programming, and high-performance numerical packages. JuliaCon began as a small 2014 community event and, by the 2016 invitation post, the organizers described growth from about 75 attendees in 2014 to about 225 in 2015.

The Julia ecosystem is tightly coupled to packages and registries: users install the language from system package managers or official binaries, then use Julia's built-in package manager for reproducible environments and project-specific dependencies.

### How it is used

Developers use the packaged `julia` command for the REPL, script execution, package management, precompilation, notebooks, and project environments. On Unix-like systems, startup customization commonly lives at `~/.julia/config/startup.jl`, which makes the package feel both like a runtime and a personal computing environment.

### Why package nerds care

Julia is significant to package nerds because it layers a language-native package manager on top of OS package managers. The outer package installs the compiler and standard tooling, while Julia's registries, manifests, artifacts, and binary wrappers manage the fast-moving scientific package ecosystem inside user projects.

### Timeline

- 2009: The Julia team began work on the language, according to the 2012 launch essay's two-and-a-half-year framing.
- 2012: Julia was publicly announced on February 14.
- 2014: The first JuliaCon was held in Chicago.
- 2018: Julia 1.0 was released during JuliaCon 2018.
- 2022: The Julia community marked ten years since the public announcement with adoption stories from users.

### Related projects

- LLVM is central to Julia's compilation strategy.
- Pkg is Julia's built-in package manager and environment tool.
- SciML, JuMP, and the broader Julia package registry are major ecosystem pillars.

### Sources

- <https://docs.julialang.org/>
- <https://formulae.brew.sh/formula/julia>
- <https://github.com/JuliaLang/julia>
- <https://julialang.org/blog/2012/02/why-we-created-julia/>
- <https://julialang.org/blog/2016/05/juliacon-invitation/>
- <https://julialang.org/blog/2018/08/one-point-zero/>
- <https://julialang.org/blog/2022/02/10years/>


## Security Notes

generalized runtime or code generation signal.

- **Geiger risk:** yellow / medium
- generalized runtime or code generation signal


## Configuration and credential file locations

These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval.


## Configuration files

- Unix: ~/.julia/config/startup.jl

## Combined YAML source

View the package source record on GitHub. [combined/julia.yml](https://github.com/automic-vault/db/blob/main/combined/julia.yml)


## Sources

- Nucleus package database
- Geiger risk classifier
- curated configuration and credential file locations
- curated package history
- pkgdb category and tag curation