MSL
AI-Aware Scripting for Businesses

Build AI-powered business automation with 8x less code than Python using .mcn files. The only AI-first scripting language that works both standalone and embedded.

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workflow.mcn
// Customer onboarding in 5 lines
var customer = request_data
query("INSERT INTO customers VALUES (?)", (customer.name))
trigger("https://crm.com/api/create", customer)
var welcome = ai("Create welcome email for " + customer.name)
trigger("https://mail.com/send", {"to": customer.email, "body": welcome})

Why Choose MSL?

AI Integration

Built-in AI functions with context awareness. No complex API setup required.

8x Less Code

Reduce cognitive load with domain-specific syntax for business automation.

Parallel Tasks

Async task execution with simple await syntax for workflow automation.

Native SQL

Database operations with batch support and automatic connection management.

Package System

Modular functionality with built-in packages for common business operations.

One-Click Deploy

Serve MSL scripts as REST APIs with a single command.

MSL vs Python: The Right Tool for the Job

MSL (Macincode Scripting Language) isn't competing with Python—it's abstracting Python for specific use cases, just like SQL abstracts database operations. Using .mcn files, it's the only AI-first scripting language that works both standalone and embedded.

Target Audience

MSL: Business users, workflow automators, Gen-Z developers who want speed
Python: Full-stack developers, data scientists, system programmers

Code Reduction

Task Python Lines MSL Lines Advantage
Database + API + AI 25+ 3 8x reduction
Workflow automation 40+ 5 8x reduction
Business logic 30+ 4 7x reduction

Python (Verbose)

import requests
import sqlite3
import openai
from datetime import datetime

def create_user_workflow(name, email):
    # Database setup
    conn = sqlite3.connect('app.db')
    cursor = conn.cursor()
    
    # Insert user
    cursor.execute("INSERT INTO users (name, email) VALUES (?, ?)", (name, email))
    conn.commit()
    
    # Send notification
    response = requests.post('https://api.slack.com/webhook', 
                           json={'text': f'New user: {name}'})
    
    # AI analysis
    client = openai.OpenAI()
    ai_response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": f"Analyze user: {name}"}]
    )
    
    return ai_response.choices[0].message.content

MSL (Minimal)

var name = "Alice"
var email = "alice@company.com"

query("INSERT INTO users (name, email) VALUES (?, ?)", (name, email))
trigger("https://api.slack.com/webhook", {"text": "New user: " + name})
var analysis = ai("Analyze user: " + name)

When to Use What?

Use MSL For:

  • Business Workflows
  • API Automation
  • AI-powered tools
  • Rapid Prototyping
  • Workflow Automation
  • Embedded Scripting

Use Python For:

  • Machine Learning
  • Web Applications
  • Data Analysis
  • System Programming
  • Complex Applications

See MSL in Action

Database CRUD

use "db"

var user = {"name": "John", "age": 25}
query("INSERT INTO users VALUES (?, ?)", (user.name, user.age))

var users = query("SELECT * FROM users WHERE age > ?", (18))
log "Found " + users.length + " adult users"

AI Integration

use "ai"

var feedback = "The product is amazing but shipping was slow"
var sentiment = analyze_sentiment(feedback)

if sentiment.score > 0.5
    var response = ai("Generate positive customer response")
    trigger("https://crm.com/respond", {"message": response})

Parallel Tasks

task "email" "trigger" "https://mail.api.com/send" {"to": "user@example.com"}
task "log" "query" "INSERT INTO logs VALUES (?)" ("Action completed")
task "ai" "ai" "Summarize today's activities"

var results = await "email" "log" "ai"
log "All tasks completed: " + results

Workflow Automation

var customer = request_data

query("INSERT INTO customers VALUES (?)", (customer.name))
trigger("https://crm.com/api/create", customer)

var welcome = ai("Create welcome email for " + customer.name)
trigger("https://mail.com/send", {"to": customer.email, "body": welcome})

Choose Your Development Style

Backend-Only (Traditional)

Perfect for quick scripts, APIs, and automation tasks

# Execute scripts directly
python msl_cli.py examples/database_crud.msl

# Start REPL mode
python msl_cli.py --repl

# Serve as API
python msl_cli.py script.msl --serve --port 8000
✓ Quick Start ✓ No Setup ✓ Direct Execution

Frontend + Backend (Full-Stack)

Build complete applications with React frontend and MSL backend

# React component calls MSL backend
                        const response = await fetch('/api/msl', {
  method: 'POST',
  body: JSON.stringify({script: 'ai("Hello world")'})
})

# MSL handles the business logic
var result = ai("Generate user greeting")
return {"message": result}
React Integration User Functions Full-Stack

New: User-Defined Functions

Create reusable functions with parameters, return values, and proper variable scoping. Perfect for both backend scripts and frontend integration.

Ready to Build with MSL?

Start simple with backend scripts or use the interactive web playground. The only AI-first scripting language that works both standalone and embedded.

Get Started Now