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Pyrl (Python + Perl) is a modern hybrid programming language that combines Perl's expressive power with Python's readability and simplicity. The language uses Perl's sigil-based variable system ($scalar, @array, %hash, &function) together with Python's control flow syntax and indentation.

• 🔥 Sigil Variables — intuitive type recognition by prefix
• 🐍 Python Syntax — familiar if, for, while, def constructs
• 🐪 Perl Power — regular expressions, built-in operators
• 🗄️ SQLite Integration — native database operations
• 🌐 Web Server — built-in HTTP server with REST API
• 🤖 AI Model — pretrained model for code generation

$name = "Pyrl"          # Scalar ($)
@numbers = [1, 2, 3]    # Array (@)
%config = {             # Hash (%)
    "host": "localhost",
    "port": 8080
}
&handler = {            # Function (&)
    return "Hello!"
}

def greet($name):
    if $name:
        return "Hello, " + $name + "!"
    else:
        return "Hello, World!"

for $i in range(5):
    print($i)

$text = "Hello, World!"
if $text =~ m/World/:
    print("Found!")

$result = $text =~ s/World/Pyrl/

$app = {
    handle: &handle_request
}

def handle_request($req):
    %response = {
        "status": 200,
        "body": "Hello from Pyrl!"
    }
    return %response

# Clone repository
git clone https://github.com/pyrl-lang/pyrl.git
cd pyrl

# Install dependencies
pip install -r requirements.txt

# Interactive console
python pyrl_cli.py

# Execute file
python pyrl_cli.py examples/01_hello_world.pyrl

# Web server
python scripts/run_web_app.py examples/web_server_auth.pyrl

# Start server
docker-compose up -d server

# Console
docker-compose run console

Plugin for Visual Studio Code with syntax highlighting, autocompletion, and snippets.

cd vscode-pyrl
code --install-extension .

Features:

• 🎨 Syntax highlighting
• 📝 Quick construction snippets
• 🔍 Variable autocompletion

Train your own model for Pyrl code generation:

# Generate model
python scripts/generate_model.py

# Training
python scripts/train_model.py --epochs 10

# Inference
python scripts/model_inference.py

The repository includes a pretrained model:

• Location: models/pyrl-model/
• Tokenizer: BPE, 10,000 tokens
• Architecture: Transformer-based

pyrl/
├── src/
│   └── core/
│       ├── lark_parser.py      # LALR parser
│       └── vm/
│           ├── vm.py           # Virtual machine
│           ├── builtins.py     # Built-in functions
│           ├── builtins_db.py  # SQLite functions
│           └── builtins_http.py # HTTP functions
├── vscode-pyrl/                # VSCode extension
├── scripts/
│   ├── generate_model.py       # Model generator
│   └── train_model.py          # Model training
├── models/pyrl-model/          # Pretrained model
├── examples/                   # Code examples
├── docker/                     # Docker configs
└── documents/                  # Documentation

# Run all tests
make test

# Specific test
python -m pytest tests/test_vm.py -v

See PROJECT_ROADMAP.md for development plans.

• ✅ SQLite integration
• ✅ Web server with REST API
• ✅ AI model for code generation
• ✅ VSCode extension

• 🔄 JIT compilation
• 🔄 Standard library
• 🔄 Package manager
• 🔄 Debugger

We welcome contributions!

1. Fork the repository
2. Create a branch (git checkout -b feature/amazing)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing)
5. Open a Pull Request

MIT License — use freely for any purpose.

Pyrl — bringing the best of Python and Perl together