Prompt Engine

🙏 Inspired by ruvnet/Prompt-Engine

This is a complete TypeScript reimplementation that extends the original template concept.

Prompt Engine

A modern, TypeScript-based prompt template engine with built-in security, monitoring, and minimal dependencies.

Features

TypeScript First: Full type safety and modern JavaScript features
Minimal Dependencies: Only 3 runtime dependencies (zod, isomorphic-dompurify, @google/generative-ai)
Progressive Enhancement: Optional YAML/TOML support via dynamic imports
Built-in Security: XSS protection, injection detection, and PII masking
Production Monitoring: Native Node.js diagnostics channel integration
Cost Tracking: Automatic token usage and cost calculation for major LLM providers
VS Code Integration: JSON schema support with IntelliSense
CLI Tool: Built-in command-line interface using Node.js native features

Quick Start

# Install
npm install prompt-engine

# Validate a configuration
npx prompt-engine validate config.json

# Initialize a new configuration
npx prompt-engine init my-config.json

Configuration

Create a prompt-engine.config.json file:

{
  "version": "2.0",
  "engine": {
    "defaults": {
      "temperature": 0.7,
      "maxTokens": 2048
    },
    "logging": {
      "level": "info"
    }
  },
  "prompts": [
    {
      "id": "summarize",
      "model": "gpt-4",
      "template": "Summarize the following text: {{text}}",
      "variables": {
        "text": ""
      }
    }
  ]
}

Monitoring

The engine includes built-in observability using Node.js diagnostics channels:

import { monitor, enableAutomaticCostTracking, getMonitoringSummary } from 'prompt-engine/monitoring';

// Enable automatic cost calculation
enableAutomaticCostTracking();

// Listen to events
monitor.on('prompt.complete', (metrics) => {
  console.log(`Cost: $${metrics.cost}, Tokens: ${metrics.totalTokens}`);
});

// Get summary
console.log(getMonitoringSummary());

Security

Built-in security features protect against common vulnerabilities:

import { sanitizePrompt } from 'prompt-engine/security';

// Automatically sanitizes XSS, detects injections, and masks PII
const safe = await sanitizePrompt(userInput);

Development

# Install dependencies
npm install

# Run tests (Node.js built-in test runner)
npm test

# Build
npm run build

# Type check
npm run typecheck

Requirements

Node.js >= 18.0.0
TypeScript >= 5.0

API

See API.md for detailed API documentation.

Migration

Upgrading from v1.x? See MIGRATION.md for upgrade instructions.

Why Choose This Prompt Engine?

🚀 Competitive Advantages

Minimal Dependencies: Only 3 runtime dependencies vs. dozens in alternatives
Security-First: Built-in XSS protection, injection detection, and PII masking
TypeScript Native: Full type safety and modern JavaScript features
Cost Tracking: Automatic token usage and cost calculation for all major LLMs
Production Ready: Native Node.js diagnostics, health checks, and monitoring
Developer Friendly: CLI tool, JSON schemas, and comprehensive documentation

How We Compare

Feature	This Engine	LangChain	PromptLayer	Others
Open Source	✅ MIT	✅ Yes	❌ No	Mixed
Dependencies	✅ Only 3	❌ Heavy	❌ Many	Many
Built-in Security	✅ Complete	⚡ Basic	❌ No	Limited
TypeScript Native	✅ Yes	✅ Yes	⚡ Partial	Mixed
Free to Use	✅ 100%	✅ Yes	⚡ Limited	Mixed

Philosophy

This project follows a "progressive enhancement" approach:

Core functionality works with minimal dependencies
Optional features (YAML/TOML) load on-demand
Built-in Node.js features preferred over external packages
Type safety without runtime overhead

License

MIT - See LICENSE file for details.

This project includes attribution to the original Prompt-Engine concept by ruvnet.

Examples

Simple Example

In this simple example, we demonstrate how to create a basic prompt that allows users to choose their area of interest and receive content based on their selection.

[prompt]
Author = "rUv"
name = "Area of Interest Selector"
forked_from = "ruvnet"
version = "1.0"

# initial prompt
# Purpose: The initial prompt welcomes the user to the Area of Interest Selector and explains the purpose of the bot.
# It also informs the user that the bot will demonstrate how to create a basic prompt that allows users to choose their area of interest and receive content based on their selection.
init = "Welcome to the Area of Interest Selector. This bot will demonstrate how to create a basic prompt that allows users to choose their area of interest and receive content based on their selection. Let's get started!"

[prompt.features.personalization.domains]
Description = "Choose your area of interest."
Domain_A = "Science"
Domain_B = "History"

[prompt.commands.commands]
Description = "Available commands."
help = "Get help."
choose_domain = "Choose your area of interest."

Advanced Example

In this advanced example, we demonstrate how to create a more complex prompt that includes additional personalization options, such as content complexity and interaction styles.

[prompt]
Author = "rUv"
name = "Advanced Prompt Engine Template Creator"
forked_from = "ruvnet"
version = "1.0"

# initial prompt
init = "Welcome to the Advanced Prompt Engine Template Creator. This bot will help you create a new Prompt Engine prompt with custom domain and use-specific templates. Let's get started!"

[prompt.features.personalization]
Description = "Personalized settings."

[prompt.features.personalization.domains]
Description = "Choose your area of interest."
Domain_A = "Science"
Domain_B = "History"

[prompt.features.personalization.complexity]
description = "Choose content complexity level."
Level_1 = "Basic"
Level_10 = "Advanced"

[prompt.features.personalization.interaction_styles]
Description = "Choose interaction style."
Style_A = "Text-based"
Style_B = "Voice-based"

[prompt.commands]
Description = "Available commands."
prefix = "Choose one of the following commands:"

[prompt.commands.commands]
help = "Get help."
choose_domain = "Choose your area of interest."
choose_complexity = "Choose content complexity level."
choose_interaction = "Choose interaction style."

Educational Platforms

# Prompt Engine Template for Educational Platforms

[prompt]
Author = "ChatGPT"
name = "Educational Platform"
version = "1.0.0"

# initial prompt
init = "Welcome to the Educational Platform! This bot will help you personalize your learning experience. Choose your area of interest and adapt to your level of expertise. Let's get started!"

[prompt.features.personalization]
Description = "Personalized learning experience."

[prompt.features.personalization.domains]
Description = "Choose area of interest."
Mathematics = "Learn about algebra, calculus, and geometry."
Science = "Explore physics, chemistry, and biology."

[prompt.features.personalization.complexity]
description = "Adapt to learner's level of expertise."
Level_1 = "Basic concepts and general overview."
Level_10 = "Advanced topics and in-depth analysis."

[prompt.commands]
Description = "Commands for interaction."
start = "Start the learning session."
stop = "End the learning session."

Customer Support

[prompt]
Author = "ChatGPT"
name = "Customer Support"
version = "1.0.0"

# initial prompt
init = "Welcome to Customer Support. How may I assist you today?"

[prompt.features.personalization]
Description = "Dynamic customer support."

[prompt.features.personalization.tone_styles]
Description = "Tone of the support."
Friendly = "Friendly and approachable tone."
Formal = "Formal and professional tone."

[prompt.commands]
Description = "Commands for assistance."
help = "Get help with a specific issue."
feedback = "Provide feedback about the service."

Content Exploration

[prompt]
Author = "ChatGPT"
name = "Content Exploration"
version = "1.0.0"

# initial prompt
init = "Welcome to the Content Exploration bot. This bot will help you explore different types of content in an interactive way. Let's get started!"

[prompt.features.personalization]
Description = "Interactive content exploration."

[prompt.features.personalization.presentation_styles]
Description = "Presentation of content."
List = "Display content as a list."
Grid = "Display content in a grid layout."

[prompt.commands]
Description = "Commands for navigation."
next = "Go to the next page of content."
previous = "Go to the previous page of content."
search = "Search for specific content."

Virtual Assistants

[prompt]
name = "Virtual Assistant"
author = "ChatGPT"
version = "1.0.0"

# initial prompt
init = "Welcome to Virtual Assistant. This bot provides personalized virtual assistance with conversational or command-based interaction. How can I assist you?"

[prompt.features.personalization]
Description = "Personalized virtual assistance."

[prompt.features.personalization.interaction_styles]
Description = "Interaction style with the virtual assistant."
Conversational = "Conversational and natural language interaction."
Command_Based = "Command-based interaction with specific keywords."

[prompt.commands]
Description = "Commands for virtual assistant."
ask = "Ask a question to the virtual assistant."
reminder = "Set a reminder or schedule an event."

Interactive Narratives

[prompt]
name = "Interactive Narrative"
version = "1.0.0"
Author = "ChatGPT"
forked_from = ""
init = "Welcome to the Interactive Narrative Bot. This bot will take you through a narrative adventure, where you can make choices and create your own story. Let's get started!"

[prompt.features.personalization]
Description = "Interactive storytelling experience."

[prompt.features.personalization.branches]
Description = "Branching narrative paths."
Path_A = "Follow the first narrative path."
Path_B = "Follow the second narrative path."

[prompt.commands]
Description = "Commands for interactive narrative."
choose = "Make a choice in the narrative."
continue = "Continue to the next part of the story."

Production Deployment

Environment Variables

The engine supports configuration through environment variables:

# API Keys
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
export GEMINI_API_KEY=...
export PERPLEXITY_API_KEY=pplx-...

# Engine Configuration
export PROMPT_ENGINE_DEFAULT_MODEL=gpt-4
export PROMPT_ENGINE_TEMPERATURE=0.7
export PROMPT_ENGINE_MAX_TOKENS=2048

# Security Settings
export PROMPT_ENGINE_CHECK_INJECTION=true
export PROMPT_ENGINE_CHECK_PII=false
export PROMPT_ENGINE_SANITIZE_HTML=true
export PROMPT_ENGINE_MAX_LENGTH=5000

# Rate Limiting
export PROMPT_ENGINE_RATE_LIMIT_ENABLED=true
export PROMPT_ENGINE_RATE_LIMIT_WINDOW_MS=60000
export PROMPT_ENGINE_RATE_LIMIT_MAX_REQUESTS=60

# Logging
export PROMPT_ENGINE_LOG_LEVEL=info
export PROMPT_ENGINE_LOG_FORMAT=json

Docker Support

FROM node:20-alpine

WORKDIR /app

# Copy package files
COPY package*.json ./
RUN npm ci --only=production

# Copy application
COPY dist ./dist

# Set Node.js to production
ENV NODE_ENV=production

# Run as non-root user
USER node

CMD ["node", "dist/index.js"]

Health Checks

The engine includes built-in health check endpoints:

import { createHealthCheck } from 'prompt-engine/health';

const health = createHealthCheck({
  checks: ['memory', 'api-keys', 'rate-limits']
});

// Express example
app.get('/health', async (req, res) => {
  const status = await health.check();
  res.status(status.healthy ? 200 : 503).json(status);
});

Performance Optimization

Caching

Built-in caching for prompt templates and API responses:

import { Configuration } from 'prompt-engine';

const config = await Configuration.create({
  caching: {
    enabled: true,
    ttl: 3600, // 1 hour
    maxSize: 100 // MB
  }
});

Connection Pooling

Optimized connection management for API providers:

import { createConnectionPool } from 'prompt-engine/optimization';

const pool = createConnectionPool({
  maxConnections: 10,
  keepAlive: true,
  timeout: 30000
});

Testing

Unit Testing

import { test } from 'node:test';
import { strictEqual } from 'node:assert';
import { sanitizePrompt } from 'prompt-engine/security';

test('sanitizes user input', async () => {
  const input = '<script>alert("xss")</script>';
  const result = await sanitizePrompt(input);
  strictEqual(result, '');
});

Integration Testing

# Run integration tests
npm run test:integration

# Run with coverage
npm run test:coverage

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

Fork the repository
Clone your fork
Install dependencies: npm install
Create a feature branch: git checkout -b feature/your-feature
Make your changes
Run tests: npm test
Submit a pull request

Code Style

ESLint configuration included
Prettier formatting
Conventional commits recommended

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
examples		examples
prompts		prompts
src		src
templates		templates
test		test
.env.example		.env.example
.gitignore		.gitignore
API.md		API.md
ATTRIBUTION.md		ATTRIBUTION.md
CONTRIBUTING.md		CONTRIBUTING.md
HOW_I_BUILT_THIS.md		HOW_I_BUILT_THIS.md
LICENSE		LICENSE
MIGRATION.md		MIGRATION.md
PRICING_UPDATE.md		PRICING_UPDATE.md
README.md		README.md
package-lock.json		package-lock.json
package.json		package.json
prompt-engine-update-notes.md		prompt-engine-update-notes.md
tsconfig.json		tsconfig.json

License

sabbathandco/prompt-engine-ts

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Prompt Engine

Features

Quick Start

Configuration

Monitoring

Security

Development

Requirements

API

Migration

Why Choose This Prompt Engine?

🚀 Competitive Advantages

How We Compare

Philosophy

License

Examples

Simple Example

Advanced Example

Educational Platforms

Customer Support

Content Exploration

Virtual Assistants

Interactive Narratives

Production Deployment

Environment Variables

Docker Support

Health Checks

Performance Optimization

Caching

Connection Pooling

Testing

Unit Testing

Integration Testing

Contributing

Development Setup

Code Style

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