APTL (AI Prompt Template Language)

A modern template engine designed specifically for AI system prompts

Example

Write this APTL template:

@section identity
  IDENTITY
  You are @{agentName|"AI"}, a @{agentRole} specialized in @{domain}.

  @if credentials
    Credentials:
    @each credential in credentials
      • @{credential}
    @end
  @end
@end

@section objective
  OBJECTIVE
  Your primary goal is to @{primaryGoal}.

  @if examples
    Examples of great responses:
    @each example in examples
      Input: @{example.input}
      Output: @{example.output}
    @end
  @end
@end

With this data:

const data = {
  agentName: 'CodeAssist Pro',
  agentRole: 'senior software engineer',
  domain: 'full-stack development',
  credentials: ['10+ years experience', 'TypeScript expert'],
  primaryGoal: 'write clean, maintainable code',
  examples: [
    { input: 'Optimize this loop', output: 'Use map() for transformations' },
    { input: 'Fix memory leak', output: 'Remove event listener in cleanup' }
  ]
};

And get this output:

IDENTITY
You are CodeAssist Pro, a senior software engineer specialized in full-stack development.

Credentials:
• 10+ years experience
• TypeScript expert


OBJECTIVE
Your primary goal is to write clean, maintainable code.

Examples of great responses:
Input: Optimize this loop
Output: Use map() for transformations
Input: Fix memory leak
Output: Remove event listener in cleanup

Why APTL?

  • Purpose-Built for AI - Designed for LLM system prompts, not HTML pages
  • Human-Readable - Clean syntax that makes sense at a glance
  • Template Inheritance - DRY principles with @extends and reusable snippets
  • Dynamic & Adaptive - Conditionals, loops, and context-aware rendering
  • Type-Safe - Full TypeScript support with detailed error messages
  • Production-Ready - Used in production AI systems

Quick Start

pnpm add @finqu/aptl
import { APTLEngine } from '@finqu/aptl';

const engine = new APTLEngine('gpt-5');
const output = await engine.render(template, data);

Documentation

Learn More:

Common Use Cases:

  • AI agent system prompts (identity, capabilities, behavior)
  • Context-aware chatbot responses
  • Few-shot learning with examples
  • Dynamic prompt generation
  • Prompt versioning and maintenance at scale

Contributing

Contributions are welcome! Please see our Contributing Guide.

License

This project is licensed under the MIT © 2025 Finqu