Fig Language - A Modern Scripting Language

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Fig is a dynamically strongly typed programming language designed for clarity, safety, and modern development practices. With features inspired by Go, Rust, and JavaScript, Fig aims to provide a productive development experience while maintaining strong type safety.

LanguageTutorial(zh_CN)

Features

🚀 Core Language Features

• Dynamic typing with type inference - Strong typing with minimal annotations
• Modern control flow - Full for loop support with proper scoping
• First-class functions - Lambda expressions and closures
• Rich data structures - Structs, lists, maps, and tuples
• Memory safety - No null pointer exceptions, automatic memory management

🔧 Technical Highlights

• Three-level scoping for loops - Proper variable isolation in iterations
• Smart semicolon handling - Flexible statement termination with RAII guards
• Comprehensive error handling - Detailed error messages with source locations
• Clean C++ implementation - Modern C++23 with RAII and smart pointers

🔧 Install

Installation

Prerequisites

Xmake version 3.0.0 or higher must be installed on your system.

Build Instructions

1. Clone the repository:

git clone https://git.fig-lang.cn/PuqiAR/Fig.git
# Recommend

or

git clone https://github.com/PuqiAR/Fig.git

2. Navigate to the project directory:

cd Fig

3. Build the project:

xmake build Fig

4. Run the program:

xmake run Fig [file]

Replace [file] with the path to your input file.

Language Philosophy

Fig is designed around several core principles:

Clarity over cleverness - Code should be readable first

Safety by default - Prevent common errors at compile time

Modern ergonomics - Developer experience matters

Gradual learning - Simple to start, powerful when needed

Performance Summary

Version: 0.4.2-alpha (Tree-walker Interpreter)
Test Hardware: i5-13490F, Windows 11

Execution Times for Fibonacci(30):

• Naive Recursion: 11.72s
• Memoization: 0.93ms (12,600× faster)
• Iteration: 0.37ms (31,300× faster)
• Tail Recursion: 0.40ms (29,200× faster)

Visual Comparison:

Naive Recursion  : ████████████████████████████████████████ 11.72s
Memoization      : ▉ 0.93ms
Iteration        : ▍ 0.37ms
Tail Recursion   : ▎ 0.40ms

Key Insight: Algorithm choice dominates performance in this tree-walker implementation.

Detailed Reports: English | 中文

Language Documents

see ./docs/en_US/...

We're looking for translators to help translate our project and make it accessible to more language communities.