PAL - Prompt Assembly Language

PAL (Prompt Assembly Language) is a framework for managing LLM prompts as versioned, composable software artifacts. It treats prompt engineering with the same rigor as software engineering, focusing on modularity, versioning, and testability.

This is the NodeJS port of the Python version of PAL.

⚡ Features

• Modular Components: Break prompts into reusable, versioned components
• Template System: Powerful Nunjucks-based templating with variable injection
• Dependency Management: Import and compose components from local files or URLs
• LLM Integration: Built-in support for OpenAI, Anthropic, and custom providers
• Evaluation Framework: Comprehensive testing system for prompt validation
• Rich CLI: Beautiful command-line interface with syntax highlighting
• Flexible Extensions: Use .pal/.pal.lib or .yml/.lib.yml extensions
• Type Safety: Full TypeScript support with Zod validation for all schemas
• Observability: Structured logging and execution tracking

📦 Installation

# Install with npm
npm install -g pal-framework

# Or with yarn
yarn global add pal-framework

# Or with pnpm
pnpm add -g pal-framework

📁 Project Structure

my_pal_project/
├── prompts/
│   ├── classify_intent.pal     # or .yml for better IDE support
│   └── code_review.pal
├── libraries/
│   ├── behavioral_traits.pal.lib    # or .lib.yml
│   ├── reasoning_strategies.pal.lib
│   └── output_formats.pal.lib
└── evaluation/
    └── classify_intent.eval.yaml

🚀 Quick Start

1. Create a Component Library

For a detailed guide, read this.

# libraries/traits.pal.lib
pal_version: "1.0"
library_id: "com.example.traits"
version: "1.0.0"
description: "Behavioral traits for AI agents"
type: "trait"

components:
  - name: "helpful_assistant"
    description: "A helpful and polite assistant"
    content: |
      You are a helpful, harmless, and honest AI assistant. You provide
      accurate information while being respectful and considerate.

2. Create a Prompt Assembly

For a detailed guide, read this.

# prompts/classify_intent.pal
pal_version: "1.0"
id: "classify-user-intent"
version: "1.0.0"
description: "Classifies user queries into intent categories"

imports:
  traits: "./libraries/traits.pal.lib"

variables:
  - name: "user_query"
    type: "string"
    description: "The user's input query"
  - name: "available_intents"
    type: "list"
    description: "List of available intent categories"

composition:
  - "{{ traits.helpful_assistant }}"
  - ""
  - "## Task"
  - "Classify this user query into one of the available intents:"
  - ""
  - "**Available Intents:**"
  - "{% for intent in available_intents %}"
  - "- {{ intent.name }}: {{ intent.description }}"
  - "{% endfor %}"
  - ""
  - "**User Query:** {{ user_query }}"

3. Use the CLI

# Compile a prompt
pal compile prompts/classify_intent.pal --vars '{"user_query": "Take me to google.com", "available_intents": [{"name": "navigate", "description": "Go to URL"}]}'

# Execute with an LLM
pal execute prompts/classify_intent.pal --model gpt-4 --provider openai --vars '{"user_query": "Take me to google.com", "available_intents": [{"name": "navigate", "description": "Go to URL"}]}'

# Validate PAL files
pal validate prompts/ --recursive

# Run evaluation tests
pal evaluate evaluation/classify_intent.eval.yaml

4. Use Programmatically

import { PromptCompiler, PromptExecutor, MockLLMClient } from 'pal-framework';

async function main() {
  // Set up components
  const compiler = new PromptCompiler();
  const llmClient = new MockLLMClient("Mock response");
  const executor = new PromptExecutor(llmClient);

  // Compile prompt
  const variables = {
    user_query: "What's the weather?",
    available_intents: [{"name": "search", "description": "Search for info"}]
  };

  const compiledPrompt = await compiler.compileFromFile(
    "prompts/classify_intent.pal",
    variables
  );

  console.log("Compiled Prompt:", compiledPrompt);
}

main().catch(console.error);

🧪 Evaluation System

Create test suites to validate your prompts:

# evaluation/classify_intent.eval.yaml
pal_version: "1.0"
prompt_id: "classify-user-intent"
target_version: "1.0.0"

test_cases:
  - name: "navigation_test"
    variables:
      user_query: "Go to google.com"
      available_intents: [{ "name": "navigate", "description": "Visit URL" }]
    assertions:
      - type: "json_valid"
      - type: "contains"
        config:
          text: "navigate"

🏗️ Architecture

PAL follows modern software engineering principles:

• Schema Validation: All files are validated against strict Zod schemas
• Dependency Resolution: Automatic import resolution with circular dependency detection
• Template Engine: Nunjucks for powerful variable interpolation and logic
• Observability: Structured logging with execution metrics and cost tracking
• Type Safety: Full TypeScript support with runtime validation

🛠️ CLI Commands

| Command | Description | | -------------- | ------------------------------------------------- | | pal compile | Compile a PAL file into a prompt string | | pal execute | Compile and execute a prompt with an LLM | | pal validate | Validate PAL files for syntax and semantic errors | | pal evaluate | Run evaluation tests against prompts | | pal info | Show detailed information about PAL files |

🧩 Component Types

PAL supports different types of reusable components:

• persona: AI personality and role definitions
• task: Specific instructions or objectives
• context: Background information and knowledge
• rules: Constraints and guidelines
• examples: Few-shot learning examples
• output_schema: Output format specifications
• reasoning: Thinking strategies and methodologies
• trait: Behavioral characteristics
• note: Documentation and comments

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• 📚 Documentation
• 🐛 Issues
• 💬 Discussions

🗺️ Roadmap

• [ ] PAL Registry: Centralized repository for sharing components
• [ ] Visual Builder: Drag-and-drop prompt composition interface
• [ ] IDE Extensions: VS Code and other editor integrations