AIPL – AI Prompt Language

An enterprise-grade language for AI prompt engineering and agentic workflows

Quick Start • Documentation • Examples • Security • VS Code Extension

---

📋 Table of Contents

• What is AIPL?
• Key Features
• Quick Start
• LLM Providers
• Security
• Evaluation & Testing
• Monitoring
• Documentation
• Examples
• VS Code Extension
• Installation
• CI/CD
• Contributing
• License

---

🤔 What is AIPL?

AIPL (AI Prompt Language) is an enterprise-grade programming language for working with Large Language Models (LLMs). It combines simplicity with powerful AI integration, comprehensive security, automated evaluation, and production-ready features for building, testing, and deploying AI applications.

Why AIPL?

Traditional Approach	AIPL Approach
❌ Scattered prompt strings in code	✅ Structured, reusable prompt templates
❌ Inconsistent error handling	✅ Built-in error handling and retries
❌ Manual provider switching	✅ Easy switching between 15+ LLM providers
❌ Limited workflow orchestration	✅ Sequential & parallel execution
❌ No IDE support	✅ Full VS Code integration
❌ Security vulnerabilities	✅ Built-in sandboxing & validation
❌ No quality metrics	✅ Comprehensive evaluation framework
❌ Manual deployments	✅ Automated CI/CD with release notes

---

✨ Key Features

🎯 Core Language

• Complete Programming Language – Variables, functions, control flow, error handling
• 100+ Built-in Functions – String manipulation, array operations, file I/O, date/time, HTTP, encoding, validation
• Modern Syntax – Clean, intuitive syntax similar to JavaScript/Python
• Type System ✨ – Static types, interfaces, type inference, generics, runtime validation
• Module System ✨ – Import/export, dependency management, module resolution
• Package Manager (AIPM) ✨ – Install, publish, search packages like npm
• Type Safety – Runtime type checking and validation with strict/lenient modes
• Async/Await – Native support for asynchronous operations
• Advanced Number Formats – Hexadecimal (0xFF), Binary (0b1010) support
• Template Strings – Multiline strings with variable interpolation

🤖 AI Integration

• 14+ LLM Providers – OpenAI, Anthropic, Google, Mistral, Together AI, Groq, Azure OpenAI, Perplexity, Replicate, AI21, Cohere, HuggingFace, Ollama, and more
• Prompt Management – Reusable templates with variable interpolation
• Workflow Orchestration – Build complex multi-step AI workflows with dependencies
• Streaming Support – Real-time response streaming
• Token Management – Track and optimize token usage
• Retry Logic – Automatic retry with exponential backoff
• Rate Limiting – Built-in rate limit management

🛠️ Developer Experience

• VS Code Extension – Full IDE support with syntax highlighting, IntelliSense, debugging
• Multi-Platform – JavaScript, Python, Java, Go SDKs
• CLI Tools – Command-line interface for quick execution
• Server Interfaces – HTTP, WebSocket, gRPC support
• Testing Framework – Built-in testing and validation tools

---

🚀 Quick Start

Installation

# Using npm
npm install -g aipl

# Using pip
pip install aipl

# Using homebrew
brew install aipl

Your First AIPL Program

// hello.aipl - Your first AI program

// Call an LLM
let response = llm_call("openai", "What is the meaning of life?", {
  model: "gpt-4",
  temperature: 0.7
})

// Print the result
print(response.text)

Run it:

aipl hello.aipl

More Complex Example

// Multi-provider AI workflow

// Step 1: Research with GPT-4
let research = llm_call("openai", "Research quantum computing breakthroughs in 2024", {
  model: "gpt-4",
  maxTokens: 2000
})

// Step 2: Summarize with Claude
let summary = llm_call("anthropic", `Summarize in 3 bullet points: ${research.text}`, {
  model: "claude-3-5-sonnet-20241022"
})

// Step 3: Fast fact-check with Groq
let factCheck = llm_call("groq", `Fact-check these claims: ${summary.text}`, {
  model: "llama-3.1-70b-versatile"
})

// Output results
print("=== Research Summary ===")
print(summary.text)
print("\n=== Fact Check ===")
print(factCheck.text)

---

🌐 LLM Providers

AIPL integrates with 10 LLM providers out of the box:

Cloud Providers

Provider	Models	Best For	Speed	Cost
OpenAI	GPT-4, GPT-3.5-turbo	Complex reasoning, general purpose	Medium	$$$
Anthropic	Claude 3 (Opus, Sonnet, Haiku)	Long context, safe responses	Medium	$$$
Google	Gemini 2.0, Gemini Pro	Multimodal, fast responses	Fast	$$
Mistral AI ✨	Mistral Large, Medium	European compliance, cost-effective	Fast	$$
Together AI ✨	Llama 3, Mixtral, CodeLlama	Open-source models	Fast	$
Groq ✨	Llama 3.1, Mixtral	Ultra-fast inference (100ms)	⚡ Ultra-Fast	$$
Cohere	Command, Command R+	Enterprise, embeddings	Medium	$$
HuggingFace	10,000+ models	Experimentation, variety	Varies	Free/$

Local Providers

Provider	Models	Best For	Speed	Cost
Ollama	Llama, Mistral, Phi, etc.	Privacy, offline, development	Fast	Free
Llama ✨	Llama 3.2, 3.1, CodeLlama	Direct local access	Fast	Free

✨ = New in v1.1

Quick Provider Switching

// Development - use local Ollama
let dev_result = llm_call("ollama", "Test prompt")

// Production - use GPT-4
let prod_result = llm_call("openai", "Test prompt", {model: "gpt-4"})

// Speed-critical - use Groq
let fast_result = llm_call("groq", "Test prompt", {model: "llama-3.1-70b-versatile"})

// Cost-sensitive - use Together AI
let cheap_result = llm_call("together", "Test prompt", {model: "meta-llama/Llama-3-70b-chat-hf"})

---

🔒 Security

AIPL includes enterprise-grade security features to protect your AI applications:

Security Features

• 🛡️ Input Validation – Automatic sanitization of all inputs (prompts, code, URLs, files, JSON)
• 📦 Sandboxed Execution – Isolated VM environment with no file system or process access
• ⏱️ Resource Limits – Configurable CPU time, memory, and execution limits
• 🚦 Rate Limiting – Built-in rate limiting to prevent abuse (100 req/min default)
• 🔐 Encryption – AES-256 encryption for sensitive data
• 🌐 Domain Control – Whitelist/blacklist for external API calls
• 🔍 Vulnerability Scanning – Automatic detection of dangerous code patterns
• 📊 Security Metrics – Real-time monitoring of security events

Quick Example

const SecurityManager = require('aipl/security');

const security = new SecurityManager({
    maxExecutionTime: 30000,  // 30 seconds
    maxMemory: 512 * 1024 * 1024,  // 512MB
    rateLimitRequests: 100,
    rateLimitWindow: 60000,  // 1 minute
    allowedDomains: ['api.openai.com', 'api.anthropic.com']
});

// Validate input
const validation = security.validateInput(userPrompt, 'prompt');
if (!validation.valid) {
    console.error('Invalid input:', validation.error);
}

// Execute in sandbox
const result = await security.executeSandboxed(code, context, 5000);

📖 Full Documentation: Security Guide

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✅ Evaluation & Testing

Comprehensive evaluation framework for assessing AIPL script quality and performance:

Evaluation Metrics

• ⚡ Performance – Execution time, memory usage, LLM call efficiency
• 📝 Quality – Syntax, structure, readability, complexity analysis
• 🤖 LLM – Success rates, latency, token usage
• 🔒 Security – Vulnerability detection, security score

Quick Example

const EvaluationFramework = require('aipl/evaluation');

const evaluator = new EvaluationFramework({
    outputDir: './evaluation-results',
    passingScore: 70
});

// Evaluate a script
const script = `
agent DataProcessor {
    prompt: "Analyze: {input}"
    llm: "openai"
    temperature: 0.7
}
`;

const result = await evaluator.evaluateScript(script, {
    executionTime: 1200,
    memoryUsed: 50 * 1024 * 1024,
    llmCalls: 1
});

console.log('Score:', result.score);  // 0-100
console.log('Passed:', result.passed);
console.log('Metrics:', result.metrics);

Benchmarking

// Run benchmark suite
const benchmarks = {
    name: 'Performance Test',
    tests: [
        { name: 'Simple Agent', script: '...' },
        { name: 'Complex Workflow', script: '...' }
    ]
};

const results = await evaluator.runBenchmarks(benchmarks);
console.log('Pass Rate:', results.passRate + '%');

📖 Full Documentation: Evaluation Guide

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📊 Monitoring

Production-ready observability with comprehensive logging, metrics, and tracing:

Monitoring Features

• 📝 Structured Logging – JSON logs with multiple levels (error, warn, info, debug, trace)
• 📈 Metrics – Counters, gauges, histograms, timers
• 🔍 Distributed Tracing – Span-based tracing for complex workflows
• ⚡ Performance Monitoring – Execution time, memory usage tracking
• 📊 Prometheus Export – Export metrics in Prometheus format
• 🗄️ Log Retention – Automatic cleanup of old logs

Quick Example

const { getMonitor } = require('aipl/monitoring');

const monitor = getMonitor({
    logLevel: 'info',
    logToFile: true,
    metricsEnabled: true,
    tracingEnabled: true
});

// Logging
monitor.info('Processing request', { userId: '123' });
monitor.error('Request failed', error, { requestId: 'abc' });

// Metrics
monitor.incrementCounter('requests.total', 1, { endpoint: '/api' });
monitor.setGauge('active.users', 42);
monitor.recordHistogram('response.time', 150);

// Performance timing
const timerId = monitor.startTimer('llm.call', { provider: 'openai' });
const response = await callLLM();
const duration = monitor.endTimer(timerId);

// Tracing
const span = monitor.startSpan('process.workflow');
// ... do work ...
monitor.endSpan(span);

// Get metrics
const metrics = monitor.getAllMetrics();
const prometheus = monitor.exportPrometheus();

📖 Full Documentation: Monitoring Guide

---

📚 Documentation

Getting Started

• Installation Guide
• Quick Start Tutorial
• Language Basics
• Core Concepts

Language Reference

• Syntax Guide
• Built-in Functions
• LLM Integration
• Error Handling

Production Features

• 🔒 Security Guide – Sandboxing, validation, encryption
• ✅ Evaluation Guide – Testing, benchmarking, metrics
• 📊 Monitoring Guide – Logging, metrics, tracing

Advanced Topics

• Workflow Orchestration
• Best Practices
• Advanced Examples
• Integration Patterns

Development

• API Reference
• Testing Guide
• Contributing Guide
• SDK Development

---

💡 Examples

Basic LLM Call

let answer = llm_call("openai", "Explain quantum entanglement simply")
print(answer.text)

With Options

let creative = llm_call("mistral", "Write a haiku about coding", {
  model: "mistral-large-latest",
  temperature: 0.9,
  maxTokens: 100
})
print(creative.text)

Multi-Step Workflow

// Define a function
function analyzeText(text) {
  let sentiment = llm_call("openai", `Analyze sentiment: ${text}`)
  let summary = llm_call("anthropic", `Summarize: ${text}`)
  
  return {
    sentiment: sentiment.text,
    summary: summary.text
  }
}

// Use it
let article = readFile("article.txt")
let analysis = analyzeText(article)
print(analysis)

Error Handling

try {
  let result = llm_call("openai", "Complex query", {
    timeout: 30000
  })
  print(result.text)
} catch (error) {
  print("Error:", error.message)
  // Fallback to different provider
  let fallback = llm_call("groq", "Complex query")
  print(fallback.text)
}

Parallel Execution

// Process multiple prompts in parallel
let prompts = [
  "Explain AI",
  "Explain ML",
  "Explain Deep Learning"
]

let results = map(prompts, (prompt) => {
  return llm_call("groq", prompt, {model: "llama-3.1-8b-instant"})
})

forEach(results, (result) => {
  print(result.text)
  print("---")
})

More examples in docs/examples

---

🔄 CI/CD

AIPL includes production-ready CI/CD pipeline with automated testing, security scanning, and release management:

Features

• ✅ Automated Testing – Unit, integration, and evaluation tests
• 🔒 Security Scanning – npm audit, Snyk vulnerability scanning
• 📊 Code Quality – ESLint, Prettier, complexity analysis
• 🚀 Automated Releases – Semantic versioning with auto-generated release notes
• 📝 Release Notes – AI-powered changelog generation from commits
• 🏗️ Multi-platform – Test on Node 16.x, 18.x, 20.x
• 📈 Code Coverage – Automatic coverage reporting to Codecov
• 🎯 Performance Benchmarks – Automated performance testing

GitHub Actions Workflow

The pipeline automatically:

1. Lints code on every push
2. Scans for security vulnerabilities
3. Tests on multiple Node versions
4. Evaluates AIPL scripts for quality
5. Builds documentation
6. Runs performance benchmarks
7. Versions based on commit messages
8. Releases with auto-generated notes
9. Publishes to NPM

Commit Convention

Use conventional commits for automatic versioning:

# Breaking changes (major version bump)
git commit -m "feat!: redesign API structure"
git commit -m "feat: add feature BREAKING CHANGE: removes old API"

# New features (minor version bump)
git commit -m "feat: add support for AWS Bedrock"
git commit -m "feat: implement distributed tracing"

# Bug fixes (patch version bump)
git commit -m "fix: resolve memory leak in parser"
git commit -m "fix: correct token counting"

Automated Release Notes

Release notes are automatically generated from commits:

## Release v1.2.0

**Release Date:** 2024-12-06

### 🚨 Breaking Changes
- Redesign API structure for better consistency

### ✨ New Features
- Add support for AWS Bedrock models
- Implement distributed tracing
- Add comprehensive security sandbox

### 🐛 Bug Fixes
- Resolve memory leak in parser
- Correct token counting for Claude models
- Fix rate limiting edge cases

### 📊 Metrics
- Test Coverage: 94%
- Security Score: A+
- Performance: +15% faster

Pipeline Configuration

View the full pipeline: .github/workflows/ci-cd.yml

Running Locally

# Run tests
npm test

# Run security audit
npm audit

# Run evaluation
npm run evaluate

# Build documentation
npm run docs:build

# Run all checks
npm run prepublishOnly

---

🎨 VS Code Extension

Get the full IDE experience with the official AIPL extension:

Features

• ✨ Syntax Highlighting – Full language support
• 🔍 IntelliSense – Auto-completion for all 10 LLM providers
• 📝 Code Snippets – Quick scaffolding for common patterns
• 🐛 Debugging – Step-through debugging with breakpoints
• ▶️ Run & Test – Execute AIPL files directly from VS Code
• 🔄 Live Preview – See results in real-time
• 🎯 Provider Switching – Easily switch between LLM providers

Installation

1. Open VS Code
2. Search for “AIPL” in Extensions
3. Click Install

Or install from command line:

code --install-extension aipl.aipl-vscode

Quick Start with VS Code

1. Create a new file: hello.aipl
2. Type llm and press Tab for a snippet
3. Press Cmd/Ctrl + Shift + R to run
4. See results in the output panel

Full VS Code Guide

---

📦 Installation

Prerequisites

• Node.js 16+ (for JavaScript SDK)
• Python 3.8+ (for Python SDK)
• Java 11+ (for Java SDK)
• Go 1.19+ (for Go SDK)

Package Managers

# NPM
npm install -g aipl

# Yarn
yarn global add aipl

# PNPM
pnpm add -g aipl

# Pip
pip install aipl

# Homebrew (macOS)
brew install aipl

# Chocolatey (Windows)
choco install aipl

From Source

git clone https://github.com/isathish/aipl.git
cd aipl
npm install
npm run build
npm link

Verify Installation

aipl --version
# Output: 1.0.0

aipl --help
# Shows available commands

---

🔑 API Keys Setup

AIPL needs API keys to access LLM providers. Set them as environment variables:

Quick Setup

# Create .env file
cat > .env << EOF
# OpenAI
OPENAI_API_KEY=sk-...

# Anthropic
ANTHROPIC_API_KEY=sk-ant-...

# Google
GOOGLE_API_KEY=AI...

# Mistral AI
MISTRAL_API_KEY=...

# Together AI
TOGETHER_API_KEY=...

# Groq
GROQ_API_KEY=gsk_...

# Cohere
COHERE_API_KEY=...

# HuggingFace
HUGGINGFACE_API_KEY=hf_...
EOF

Get API Keys

• OpenAI: https://platform.openai.com/api-keys
• Anthropic: https://console.anthropic.com/
• Google: https://makersuite.google.com/app/apikey
• Mistral: https://console.mistral.ai/
• Together AI: https://api.together.xyz/signup
• Groq: https://console.groq.com/
• Cohere: https://dashboard.cohere.com/api-keys
• HuggingFace: https://huggingface.co/settings/tokens
• Ollama: No API key needed (local)

---

🧪 Testing

AIPL includes a comprehensive testing framework:

// test_example.aipl

// Test basic LLM call
test("OpenAI responds correctly", () => {
  let result = llm_call("openai", "Say hello")
  assert(result.text.length > 0)
  assert(result.model == "gpt-4")
})

// Test with multiple providers
test("All providers work", () => {
  let providers = ["openai", "anthropic", "groq"]
  
  forEach(providers, (provider) => {
    let result = llm_call(provider, "Test")
    assert(result.text != null)
  })
})

Run tests:

aipl test test_example.aipl

Full Testing Guide

---

🏗️ Project Structure

aipl/
├── src/                      # Core interpreter
│   ├── lexer.js             # Tokenizer
│   ├── parser.js            # AST parser
│   ├── interpreter.js       # Execution engine
│   ├── llm.js              # LLM provider integrations
│   └── builtins.js         # Built-in functions
├── aipl/                    # Additional resources
│   ├── docs/               # Comprehensive documentation
│   └── sdk/                # SDK implementations
├── vscode-aipl/            # VS Code extension
├── cli/                    # Command-line interface
├── server/                 # Server implementations
├── examples/               # Example AIPL programs
└── docs/                   # Project documentation

---

🤝 Contributing

We welcome contributions! Here’s how to get started:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and add tests
4. Commit: git commit -m 'Add amazing feature'
5. Push: git push origin feature/amazing-feature
6. Open a Pull Request

Development Setup

# Clone your fork
git clone https://github.com/YOUR_USERNAME/aipl.git
cd aipl

# Install dependencies
npm install

# Run tests
npm test

# Build
npm run build

# Link for local development
npm link

Guidelines

• Follow existing code style
• Add tests for new features
• Update documentation
• Keep commits atomic and well-described

Contributing Guide

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🤖 Multi-Agent Orchestration

AIPL v1.2 introduces powerful multi-agent orchestration capabilities for building complex AI workflows.

Agent Types

• Coordinator – Manages workflow execution and task distribution
• Worker – Executes assigned tasks
• Supervisor – Monitors and validates agent performance
• Specialist – Handles domain-specific tasks
• Validator – Verifies output quality

Features

const { AgentOrchestrator, Agent } = require('aipl/multi-agent');

// Create orchestrator
const orchestrator = new AgentOrchestrator({
  enableLoadBalancing: true,
  enableFailover: true,
  healthCheckInterval: 10000
});

// Register agents
const planner = new Agent('planner-1', 'coordinator', ['planning']);
const executor = new Agent('executor-1', 'worker', ['execution']);

orchestrator.registerAgent(planner);
orchestrator.registerAgent(executor);

// Define workflow with dependencies
const workflow = {
  tasks: [
    { id: 'analyze', capability: 'planning', handler: analyzeTask },
    { id: 'execute', capability: 'execution', handler: executeTask }
  ],
  dependencies: { execute: ['analyze'] }
};

// Execute workflow
orchestrator.start();
const result = await orchestrator.executeWorkflow(workflow);

Key Capabilities:

• Task dependency graphs
• Parallel execution
• Load balancing
• Automatic failover
• Message routing
• Shared state management
• Health monitoring

---

⛓️ Advanced Prompt Chaining

Build sophisticated multi-step prompts with context passing and conditional routing.

Chain Types

const { ChainBuilder } = require('aipl/prompt-chain');

// Create a complex chain
const chain = new ChainBuilder('analysis')
  // Step 1: Initial prompt
  .prompt('summarize', 'Summarize: {{input}}')
  
  // Step 2: Transform result
  .transform('extract', (text) => ({
    wordCount: text.split(' ').length,
    sentences: text.split('.').length
  }))
  
  // Step 3: Conditional branching
  .if('check-length', 'variables.wordCount > 100',
    [{ type: 'prompt', template: 'Provide detailed analysis' }],
    [{ type: 'prompt', template: 'Provide brief analysis' }]
  )
  
  // Step 4: Parallel processing
  .parallel('multi-analysis', [
    { type: 'prompt', template: 'Analyze sentiment: {{summarize}}' },
    { type: 'prompt', template: 'Extract keywords: {{summarize}}' }
  ])
  
  // Step 5: Aggregate results
  .aggregate('combine', 'object', ['check-length', 'multi-analysis'])
  
  // Set variables and execute
  .withVariables({ input: 'Your text here' })
  .build();

const result = await chain.execute();

Step Types:

• prompt – LLM prompt execution
• transform – Data transformation
• conditional – If/else branching
• aggregate – Combine multiple results
• parallel – Execute steps simultaneously
• loop – Iterate over data
• branch – Multi-way conditional

---

🔍 Vector Database Integration

Native support for multiple vector databases enabling RAG (Retrieval-Augmented Generation).

Supported Databases

• Pinecone – Cloud-native vector database
• Weaviate – GraphQL-based vector search
• Qdrant – High-performance vector database
• Chroma – Lightweight embedding database
• Memory – In-memory for testing

RAG Implementation

const { VectorDBFactory, RAGHelper } = require('aipl/vector-db');

// Connect to vector database
const vectorDB = VectorDBFactory.createClient('pinecone', {
  apiKey: process.env.PINECONE_API_KEY,
  environment: 'us-west1-gcp',
  indexName: 'my-docs'
});

await vectorDB.connect();

// Create RAG helper
const rag = new RAGHelper(vectorDB, embeddingFunction);

// Index documents
await rag.indexDocuments('knowledge-base', [
  { 
    id: 'doc1', 
    text: 'AIPL is a powerful prompt engineering language',
    metadata: { category: 'docs', version: '1.2' }
  },
  {
    id: 'doc2',
    text: 'Multi-agent orchestration enables complex workflows',
    metadata: { category: 'features', version: '1.2' }
  }
]);

// Generate with context retrieval
const response = await rag.generateWithContext(
  'knowledge-base',
  'What is AIPL?',
  llmFunction,
  {
    contextLimit: 5,
    promptTemplate: 'Context: {{context}}\n\nQuestion: {{query}}\n\nAnswer:'
  }
);

console.log(response.answer);
console.log('Context used:', response.context);

Features:

• Unified API across databases
• Semantic search
• Metadata filtering
• Batch operations
• Cosine/Euclidean/Dot product similarity

---

🤝 Real-Time Collaboration

Enable multiple users to collaborate on AIPL scripts in real-time.

Collaboration Server

const { CollaborationServer } = require('aipl/collaboration');

// Start collaboration server
const server = new CollaborationServer({
  port: 8080,
  maxUsersPerDocument: 50,
  enableAuth: true,
  authHandler: async (credentials) => ({
    success: true,
    userId: credentials.userId,
    userName: credentials.userName
  })
});

server.start();

// Listen for events
server.on('started', () => {
  console.log('Collaboration server running on port 8080');
});

server.on('conversion', (data) => {
  console.log('User action:', data);
});

Client Usage

const ws = new WebSocket('ws://localhost:8080');

// Join document
ws.send(JSON.stringify({
  type: 'join',
  payload: {
    documentId: 'script-123',
    userId: 'user-456',
    userName: 'John Doe'
  }
}));

// Send edit
ws.send(JSON.stringify({
  type: 'edit',
  payload: {
    type: 'insert',
    position: 10,
    content: 'new code'
  }
}));

// Update cursor position
ws.send(JSON.stringify({
  type: 'cursor',
  payload: { line: 5, column: 10 }
}));

Features:

• Operational Transformation (OT) for conflict resolution
• Presence awareness (cursors, selections)
• Document locking
• Version history
• Real-time chat
• User color coding

---

☁️ Cloud Deployment

Deploy AIPL applications to AWS, Azure, GCP, or Kubernetes with a single command.

AWS Lambda Deployment

const { CloudDeploymentFactory } = require('aipl/cloud-deploy');

const deployer = CloudDeploymentFactory.createDeployer('aws', {
  projectName: 'my-aipl-app',
  region: 'us-east-1',
  strategy: 'serverless',
  env: {
    OPENAI_API_KEY: process.env.OPENAI_API_KEY
  }
});

const result = await deployer.deploy();
console.log('Deployed to:', result.url);

Azure Functions

const deployer = CloudDeploymentFactory.createDeployer('azure', {
  projectName: 'my-aipl-app',
  resourceGroup: 'aipl-rg',
  location: 'eastus',
  strategy: 'serverless'
});

await deployer.deploy();

Kubernetes

const deployer = CloudDeploymentFactory.createDeployer('kubernetes', {
  projectName: 'my-aipl-app',
  namespace: 'production',
  replicas: 3,
  containerImage: 'myregistry/aipl-app:latest'
});

await deployer.deploy();

Deployment Strategies:

• Serverless (Lambda, Functions, Cloud Functions)
• Container (ECS, ACI, Cloud Run)
• VM (EC2, Azure VMs, Compute Engine)
• Kubernetes (any cluster)

Features:

• Terraform infrastructure-as-code
• Automated configuration
• Resource management
• Status monitoring
• Easy teardown

---

🧪 A/B Testing Framework

Test different prompts and configurations to optimize AI performance.

Create Experiments

const { ABTestingManager } = require('aipl/ab-testing');

const manager = new ABTestingManager();

// Create experiment
const experiment = manager.createExperiment('prompt-optimization', {
  allocationStrategy: 'weighted',
  trafficPercentage: 50,
  targetMetric: 'conversion_rate'
});

// Add variants
experiment.addVariant({
  id: 'control',
  name: 'Original Prompt',
  weight: 1,
  prompt: 'Generate a summary of: {{text}}',
  parameters: { temperature: 0.7 }
});

experiment.addVariant({
  id: 'variant-a',
  name: 'Improved Prompt',
  weight: 2,
  prompt: 'Create a concise summary focusing on key points: {{text}}',
  parameters: { temperature: 0.5 }
});

experiment.addVariant({
  id: 'variant-b',
  name: 'Detailed Prompt',
  weight: 1,
  prompt: 'Provide a comprehensive summary with examples: {{text}}',
  parameters: { temperature: 0.9 }
});

// Start experiment
experiment.start();

Allocate and Track

// Allocate variant to user
const variant = experiment.allocateVariant('user-123');
console.log('Using variant:', variant.name);

// Execute with variant prompt
const result = await executeLLM(variant.prompt, variant.parameters);

// Record conversion (e.g., user satisfaction)
if (userSatisfied) {
  experiment.recordConversion('user-123');
}

// Get results with statistical analysis
const results = experiment.getResults();
console.log('Winner:', results.winner);
console.log('Statistics:', results.statistics);

Features:

• Multiple allocation strategies (random, weighted, user-based, sequential)
• Statistical significance testing (z-score, p-value)
• Confidence intervals
• Traffic percentage control
• Custom metrics
• Real-time analytics

---

🏢 Enterprise Features

Production-ready security, compliance, and access control.

Role-Based Access Control (RBAC)

const { EnterpriseManager, Permission } = require('aipl/enterprise');

const enterprise = new EnterpriseManager();

// Assign roles
enterprise.rbac.assignRole('user-123', 'developer');
enterprise.rbac.assignRole('user-456', 'operator');
enterprise.rbac.assignRole('user-789', 'auditor');

// Check permissions
if (enterprise.checkPermission('user-123', Permission.DEPLOY_SCRIPT)) {
  await deployScript();
}

// Authorize with audit logging
await enterprise.authorize(
  'user-123',
  Permission.EXECUTE_SCRIPT,
  { type: 'script', id: 'production-workflow' },
  { ip: '192.168.1.100', sessionId: 'sess-abc' }
);

Built-in Roles:

• Admin – Full access
• Developer – Development and deployment
• Operator – Operations and monitoring
• Viewer – Read-only access
• Auditor – Audit and compliance

Permissions:

• Resource management (create, read, update, delete)
• User management
• Script execution and deployment
• Configuration changes
• Secrets management
• Audit log access

Single Sign-On (SSO)

const enterprise = new EnterpriseManager({
  ssoEnabled: true,
  sso: {
    provider: 'saml', // or 'oauth2', 'oidc'
    issuer: 'https://idp.example.com',
    audience: 'aipl-production',
    certificate: fs.readFileSync('cert.pem'),
    callbackUrl: 'https://aipl.example.com/auth/callback'
  }
});

// Initiate SSO authentication
const authRequest = await enterprise.authenticateSSO(request);
// Redirect user to authRequest.redirectUrl

// Validate SSO response
const user = await enterprise.validateSSOResponse(response);
console.log('Authenticated user:', user.email);

Supported Providers:

• SAML 2.0
• OAuth 2.0
• OpenID Connect

Audit Logging

// Automatic audit logging for all actions
await enterprise.audit.log(
  'script_executed',
  { 
    userId: 'user-123',
    username: 'john.doe',
    ip: '192.168.1.100',
    userAgent: 'Mozilla/5.0...'
  },
  {
    type: 'script',
    id: 'workflow-456',
    name: 'production-pipeline'
  },
  {
    duration: 5234,
    status: 'success'
  }
);

// Query audit log
const events = enterprise.audit.query({
  eventType: 'script_executed',
  userId: 'user-123',
  startDate: Date.now() - 30 * 24 * 60 * 60 * 1000, // Last 30 days
  limit: 100
});

// Export audit log
const csvData = enterprise.audit.export('csv');
await fs.writeFile('audit-log.csv', csvData);

Compliance Reporting

// Generate compliance report
const report = await enterprise.getComplianceReport(
  startDate,
  endDate
);

console.log('Total events:', report.summary.totalEvents);
console.log('Unique users:', report.summary.uniqueActors);
console.log('Unauthorized attempts:', report.summary.unauthorizedAttempts);

// Compliance checks
console.log('GDPR compliant:', report.compliance.gdpr.compliant);
console.log('SOX compliant:', report.compliance.sox.compliant);
console.log('HIPAA compliant:', report.compliance.hipaa.compliant);

Compliance Standards:

• GDPR (data access logging)
• SOX (segregation of duties)
• HIPAA (access control and audit)

---

📊 Roadmap

v1.1 (Completed ✅ – Q4 2024)

• ✅ Add 4 new LLM providers (Mistral, Together, Groq, Llama)
• ✅ Enhanced VS Code extension with rich UI
• ✅ Comprehensive documentation
• ✅ Type system with interfaces and generics
• ✅ Module system with imports/exports
• ✅ Package manager (AIPM) with CLI
• ✅ Security module with sandboxing
• ✅ Evaluation framework with metrics
• ✅ Monitoring system with observability
• ✅ CI/CD pipeline with automated releases

v1.2 (In Progress 🚧 – Q1 2025)

• 🚧 Multi-agent orchestration system
• 🚧 Advanced prompt chaining with context
• 🚧 Vector database integration (Pinecone, Weaviate, Qdrant, Chroma)
• 🚧 Real-time collaboration features
• 🚧 Cloud deployment tools (AWS, Azure, GCP)

v2.0 (Planned 📋 – Q2 2025)

• 📋 Browser-based playground with Monaco editor
• 📋 Prompt versioning & A/B testing framework
• 📋 Enterprise features (SSO, RBAC, audit logs)
• 📋 Mobile SDK support (iOS & Android)
• 📋 AI model fine-tuning integration

Full Roadmap

---

📄 License

MIT License – see LICENSE file for details