AAABuilder - Advanced AI/ML Agent Framework

AAABuilder (Advanced AI Agent Builder) is a comprehensive, production-ready AI/ML development framework that transforms complex AI workflows into scalable APIs. Built with TypeScript-first development, it provides unified support for all major AI/ML model types including LLMs, Computer Vision, Audio Processing, Traditional ML, and more.

What's New in v0.0.1

Major New Features

• Unified Provider System: Single interface for ALL AI/ML model types
• Advanced Debugging Tools: Step-by-step debugging with breakpoints and variable inspection
• Comprehensive Testing Framework: Automated test generation and performance benchmarking
• Project Scaffolding: Interactive project creation with templates
• Auto-Documentation: Generate comprehensive docs in multiple formats
• Interactive Playground: Real-time agent development environment
• Enterprise Security: JWT auth, rate limiting, encryption, and audit logging
• Model Optimization: Quantization, pruning, caching, and GPU acceleration
• Advanced Audio Processing: Real-time streaming, speaker identification, emotion detection

Table of Contents

• Overview
• Key Features
• Architecture
• Quick Start
• Installation
• Usage
• Provider System
• Development Tools
• Examples
• API Reference
• Deployment
• Contributing
• License

Overview

AAABuilder addresses the growing complexity of AI/ML development by providing a unified framework that handles everything from simple text generation to complex multimodal AI systems. Unlike other solutions that focus on specific domains, AAABuilder offers comprehensive support across all AI/ML paradigms with enterprise-grade features.

Why Choose AAABuilder?

| Feature | AAABuilder | LangChain | AutoGen | CrewAI | Flowise | |---------|------------|-----------|---------|--------|---------| | Model Support | All AI/ML types | LLM-focused | LLM-focused | LLM-focused | LLM-focused | | Vision Models | ✓ Full support | ✗ Limited | ✗ Limited | ✗ Limited | ✗ Limited | | Audio Processing | ✓ Advanced pipeline | ✗ None | ✗ None | ✗ None | ✗ None | | Traditional ML | ✓ Complete support | ✗ None | ✗ None | ✗ None | ✗ None | | Debugging | ✓ Step-by-step | ✗ None | ✗ None | ✗ None | ✗ None | | Testing | ✓ Automated framework | ✗ Manual | ✗ Manual | ✗ Manual | ✗ Limited | | TypeScript | ✓ First-class | ✗ Python-only | ✗ Python-only | ✗ Python-only | ✗ JavaScript | | Security | ✓ Enterprise-grade | ✗ Basic | ✗ Basic | ✗ Basic | ✗ Basic | | Optimization | ✓ Built-in | ✗ None | ✗ None | ✗ None | ✗ None |

Key Features

Unified Provider System

• Single Interface: Access all AI/ML models through one consistent API
• Automatic Discovery: Provider registry with automatic model detection
• Priority Routing: Intelligent provider selection based on capabilities
• Model Optimization: Built-in quantization, pruning, and caching

Advanced Audio Processing

• Real-time Streaming: WebRTC, WebSocket, gRPC backends
• Speaker Identification: ECAPA-TDNN, X-Vector, Wav2Vec2 models
• Emotion Detection: 7-emotion classification with arousal-valence
• Audio Enhancement: RNNoise, Facebook Denoiser, NVIDIA NoiseRed
• Speech Processing: OpenAI Whisper, custom ASR/TTS models

Computer Vision

• Object Detection: YOLO v5/v8/v10, Faster R-CNN, SSD
• Image Classification: ResNet, EfficientNet, MobileNet, Vision Transformers
• Segmentation: U-Net, DeepLab, Mask R-CNN
• OCR: Tesseract, EasyOCR, PaddleOCR integration
• Face Recognition: FaceNet, ArcFace, DeepFace

Traditional Machine Learning

• Scikit-learn: Classification, regression, clustering, dimensionality reduction
• XGBoost: Gradient boosting for structured data
• LightGBM: Light gradient boosting machine
• TensorFlow: Custom neural networks and Keras models
• Model Optimization: Hyperparameter tuning and model compression

Development Tools

Debugging Framework

import { AgentDebugger } from './src/debug';

const debugger = new AgentDebugger(providerRouter);

// Start debug session
const sessionId = await debugger.startDebugSession(agentContent, inputData);

// Set breakpoints
debugger.setBreakpoint(sessionId, 'step-id');

// Step through execution
const result = await debugger.stepNext(sessionId);

// Inspect variables
const variables = debugger.getSessionState(sessionId)?.variables;

Testing Framework

import { AgentTester } from './src/testing';

const tester = new AgentTester(providerRouter);

// Run test suite
const result = await tester.runTestSuite({
  name: 'My Agent Tests',
  agentContent: agentFile,
  testCases: [
    {
      name: 'Basic test',
      input: { message: 'Hello' },
      expectedOutput: { response: 'Hello back!' }
    }
  ]
});

// Performance benchmarking
const benchmark = await tester.benchmarkAgent(agentContent, 100);

Interactive Playground

# Start interactive development environment
npm run playground

# Available commands:
# load <file>     - Load agent file
# debug <file>    - Start debugging session
# test <file>     - Run tests
# validate <file> - Validate agent
# docs <file>     - Generate documentation

Project Scaffolding

# Create new project with interactive wizard
npm run create

# Create from template
npm run create:interactive

# List available templates
npm run templates

Auto-Documentation

# Generate comprehensive documentation
npm run docs

# Watch mode for development
npm run docs:watch

# Generate OpenAPI spec
npm run docs:openapi

# Generate HTML documentation
npm run docs:html

Enterprise Security

• JWT Authentication: Secure token-based authentication
• Role-Based Access Control: Granular permissions system
• Rate Limiting: Configurable request throttling
• Encryption: Data encryption at rest and in transit
• Audit Logging: Comprehensive security event tracking
• API Key Management: Secure API key handling

Architecture

AAABuilder/
├── src/                          # TypeScript source code
│   ├── providers/                # Unified provider system
│   │   ├── ProviderRouter.ts        # Central router
│   │   ├── ModelRegistry.ts         # Provider registry
│   │   ├── ModelOptimizer.ts        # Optimization framework
│   │   ├── AudioProviders.ts        # Audio processing
│   │   ├── Traditional ML/          # Scikit-learn, XGBoost, etc.
│   │   ├── Computer Vision/         # YOLO, ResNet, etc.
│   │   └── Audio Processing/        # Whisper, emotion detection
│   ├── debug/                    # Debugging tools
│   │   └── AgentDebugger.ts         # Step-by-step debugging
│   ├── testing/                  # Testing framework
│   │   └── AgentTester.ts           # Automated testing
│   ├── scaffolding/             # Project scaffolding
│   │   └── ProjectScaffolder.ts     # Template system
│   ├── documentation/            # Auto-documentation
│   │   └── DocumentationGenerator.ts
│   ├── playground/               # Interactive development
│   │   └── AgentPlayground.ts       # Real-time environment
│   └── security/                 # Security types
│       └── types.ts                 # Auth & authorization
├── lib/                          # Compiled JavaScript
├── bin/                          # CLI executables
├── examples/                     # Example agents
├── templates/                    # Project templates
└── plugins/                      # Plugin system

Quick Start

Prerequisites

• Node.js 18.0.0 or higher
• npm 8.0.0 or higher
• TypeScript 5.3+ (recommended)

Installation

# Install globally
npm install -g aaab

# Verify installation
aaab --version

Your First Agent

# Create new project
aaab create my-ai-project

# Navigate to project
cd my-ai-project

# Set API keys
export OPENAI_API_KEY="your-api-key-here"

# Start development server
npm run dev

Basic Agent Example

@agent hello-world v1
description: "Simple greeting agent"

secrets:
  - name: OPENAI_API_KEY
    type: env
    value: OPENAI_API_KEY

variables:
  name:
    type: input
    path: name
    required: true

steps:
  - id: greet
    type: llm
    provider: openai
    model: gpt-4o
    prompt: "Greet {name} warmly"
    save: greeting

outputs:
  message: "{greeting}"
@end

Installation

Global Installation

npm install -g aaab

Local Development

# Clone the repository
git clone https://github.com/Init-Abdellm/AAABuilder.git
cd AAABuilder

# Install dependencies
npm install

# Build the project
npm run build

# Start development server
npm run dev

Docker Installation

# Pull the image
docker pull aaabuilder/aaabuilder:latest

# Run the container
docker run -p 5000:5000 \
  -e OPENAI_API_KEY=your-key \
  aaabuilder/aaabuilder:latest

Usage

CLI Commands

# Core commands
aaab run <file>              # Execute agent file
aaab validate <file>         # Validate agent syntax
aaab serve --port 5000       # Start HTTP server

# Development tools
aaab debug <file>            # Start debugging session
aaab test <file>             # Run test suite
aaab playground              # Start interactive playground
aaab create <project>        # Create new project
aaab docs <file>             # Generate documentation

# Provider management
aaab providers --list        # List available providers
aaab providers --test        # Test provider connections
aaab providers --optimize    # Optimize model performance

Advanced Usage

# Vision operations
aaab vision --classify image.jpg
aaab vision --detect image.jpg
aaab vision --ocr document.pdf

# Audio operations
aaab audio --stt audio.wav
aaab audio --tts "Hello world"
aaab audio --emotion audio.wav
aaab audio --speaker audio.wav

# Machine learning
aaab ml --train config.json
aaab ml --predict model.pkl data.json
aaab ml --optimize model.pkl

# Vector databases
aaab vector-db --create my-db
aaab vector-db --search my-db docs "query"

Provider System

Unified Provider Interface

import { createProviderRouter, ProviderConfig } from './src/providers';

// Configure providers
const config: ProviderConfig = {
  // Traditional ML
  scikitLearn: { enabled: true, priority: 5 },
  xgboost: { enabled: true, priority: 10 },
  lightgbm: { enabled: true, priority: 8 },
  tensorflow: { enabled: true, priority: 15 },
  
  // Computer Vision
  yolo: { enabled: true, priority: 20 },
  imageClassification: { enabled: true, priority: 15 },
  imageSegmentation: { enabled: true, priority: 18 },
  visionTransformer: { enabled: true, priority: 25 },
  
  // Audio Processing
  whisper: { enabled: true, priority: 25 },
  audioEnhancement: { enabled: true, priority: 20 },
  realTimeAudio: { enabled: true, priority: 25 },
  speakerEmotion: { enabled: true, priority: 22 }
};

// Create router
const router = await createProviderRouter(config);

// Execute any model request
const response = await router.executeRequest({
  model: 'yolo-v8-detection',
  input: imageBuffer,
  parameters: { confidence: 0.5 }
});

Model Optimization

// Get optimization recommendations
const recommendations = await router.getOptimizationRecommendations('my-model');

// Optimize model
const result = await router.optimizeModel('my-model', {
  strategies: ['quantization', 'caching', 'batching'],
  quantization: {
    type: 'int8',
    targetAccuracyLoss: 0.05
  },
  caching: {
    strategy: 'lru',
    maxSize: 1000
  }
});

console.log(`Size reduction: ${result.metrics.sizeReduction}%`);
console.log(`Speed improvement: ${result.metrics.speedImprovement}x`);

Audio Processing Pipeline

// Real-time audio processing
const audioPipeline = await router.createAudioPipeline([
  { type: 'noise-reduction', model: 'rnnoise' },
  { type: 'speaker-identification', model: 'ecapa-tdnn' },
  { type: 'emotion-detection', model: 'wav2vec2-emotion' }
]);

// Process audio stream
const results = await audioPipeline.processStream(audioStream, {
  chunkSize: 1024,
  sampleRate: 16000,
  realTime: true
});

Development Tools

Debugging Framework

The debugging system provides step-by-step execution with breakpoints, variable inspection, and execution tracing.

import { AgentDebugger } from './src/debug';

const debugger = new AgentDebugger(providerRouter);

// Start debug session
const sessionId = await debugger.startDebugSession(agentContent, inputData);

// Set breakpoint on specific step
debugger.setBreakpoint(sessionId, 'process-step');

// Execute until breakpoint
await debugger.continue(sessionId);

// Inspect variables
const variables = debugger.getVariable(sessionId, 'result');

// Step through execution
const stepResult = await debugger.stepNext(sessionId);

// Get execution trace
const trace = debugger.getExecutionTrace(sessionId);

Testing Framework

Comprehensive testing with automated test generation, performance benchmarking, and mock providers.

import { AgentTester } from './src/testing';

const tester = new AgentTester(providerRouter);

// Run test suite
const testSuite = {
  name: 'My Agent Tests',
  agentContent: agentFile,
  testCases: [
    {
      name: 'Basic functionality',
      input: { message: 'Hello' },
      expectedOutput: { response: 'Hello back!' },
      timeout: 5000
    },
    {
      name: 'Error handling',
      input: { message: '' },
      shouldFail: true,
      expectedError: 'Message is required'
    }
  ]
};

const results = await tester.runTestSuite(testSuite);

// Performance benchmarking
const benchmark = await tester.benchmarkAgent(agentContent, {
  iterations: 100,
  concurrency: 10
});

Interactive Playground

Real-time development environment for agent creation and testing.

# Start playground
npm run playground

# Available commands:
agent> load examples/chatbot.agent
agent> debug examples/chatbot.agent
agent> test examples/chatbot.agent
agent> validate examples/chatbot.agent
agent> docs examples/chatbot.agent --format html
agent> help

Project Scaffolding

Interactive project creation with templates and best practices.

# Interactive project creation
npm run create

# Create from specific template
npm run create:interactive

# List available templates
npm run templates

# Available templates:
# - basic-agent
# - chatbot
# - vision-analyzer
# - audio-processor
# - ml-pipeline
# - api-server
# - webhook-processor

Auto-Documentation

Generate comprehensive documentation in multiple formats.

# Generate markdown documentation
npm run docs

# Generate HTML documentation
npm run docs:html

# Generate OpenAPI specification
npm run docs:openapi

# Watch mode for development
npm run docs:watch

# Validate documentation
npm run docs:validate

Examples

Simple Chat Agent

@agent chatbot v1
description: "AI-powered chatbot"

secrets:
  - name: OPENAI_API_KEY
    type: env
    value: OPENAI_API_KEY

variables:
  message:
    type: input
    path: message
    required: true

steps:
  - id: process
    type: llm
    provider: openai
    model: gpt-4o
    prompt: "Respond to: {message}"
    save: response

outputs:
  reply: "{response}"
@end

Vision Analysis Agent

@agent vision-analyzer v1
description: "Comprehensive image analysis"

variables:
  image:
    type: input
    path: image
    required: true

steps:
  - id: detect_objects
    type: vision
    provider: yolo
    model: yolo-v8-detection
    input: "{image}"
    save: objects

  - id: classify_image
    type: vision
    provider: image-classification
    model: resnet-50
    input: "{image}"
    save: classification

  - id: extract_text
    type: vision
    provider: ocr
    model: tesseract
    input: "{image}"
    save: text

  - id: analyze
    type: llm
    provider: openai
    model: gpt-4o-vision
    prompt: |
      Analyze this image:
      Objects detected: {objects}
      Classification: {classification}
      Text found: {text}
    save: analysis

outputs:
  objects: "{objects}"
  classification: "{classification}"
  text: "{text}"
  analysis: "{analysis}"
@end

Audio Processing Agent

@agent audio-processor v1
description: "Advanced audio analysis and enhancement"

variables:
  audio:
    type: input
    path: audio
    required: true

steps:
  - id: enhance_audio
    type: audio
    provider: audio-enhancement
    model: rnnoise
    input: "{audio}"
    save: enhanced_audio

  - id: transcribe
    type: audio
    provider: whisper
    model: whisper-large-v3
    input: "{enhanced_audio}"
    save: transcription

  - id: identify_speaker
    type: audio
    provider: speaker-identification
    model: ecapa-tdnn
    input: "{enhanced_audio}"
    save: speaker

  - id: detect_emotion
    type: audio
    provider: emotion-detection
    model: wav2vec2-emotion
    input: "{enhanced_audio}"
    save: emotion

outputs:
  transcription: "{transcription}"
  speaker: "{speaker}"
  emotion: "{emotion}"
  enhanced_audio: "{enhanced_audio}"
@end

Machine Learning Pipeline

@agent ml-pipeline v1
description: "Traditional ML model training and prediction"

variables:
  data:
    type: input
    path: data
    required: true
  operation:
    type: input
    path: operation
    required: true

steps:
  - id: train_model
    type: ml
    provider: scikit-learn
    model: random-forest
    action: train
    input: "{data}"
    parameters:
      n_estimators: 100
      max_depth: 10
    save: model
    when: "{operation} == 'train'"

  - id: predict
    type: ml
    provider: scikit-learn
    model: "{model}"
    action: predict
    input: "{data}"
    save: predictions
    when: "{operation} == 'predict'"

outputs:
  model: "{model}"
  predictions: "{predictions}"
@end

API Reference

Agent File Format

@agent agent-name v1
description: "Agent description"
version: "1.0.0"

# Trigger configuration
trigger:
  type: http | schedule | webhook
  method: GET | POST | PUT | DELETE
  path: /endpoint
  schedule: "0 */5 * * * *"  # Cron expression

# Secrets management
secrets:
  - name: API_KEY
    type: env | file | vault
    value: OPENAI_API_KEY
    required: true

# Input variables
variables:
  input_var:
    type: input | file | url
    path: data.field
    required: true
    default: "default value"
    validation:
      - type: string
      - minLength: 1
      - maxLength: 1000

# Processing steps
steps:
  - id: step_name
    type: llm | vision | audio | ml | http | condition
    provider: openai | anthropic | gemini | yolo | whisper
    model: gpt-4o | claude-3 | gemini-pro | yolo-v8
    input: "{input_var}"
    prompt: "Process: {input_var}"
    parameters:
      temperature: 0.7
      max_tokens: 1000
    save: result
    retry:
      attempts: 3
      delay: 1000
    timeout: 30000

# Conditional execution
  - id: conditional_step
    type: condition
    condition: "{result.confidence} > 0.8"
    steps:
      - id: high_confidence
        type: llm
        provider: openai
        model: gpt-4o
        prompt: "High confidence result: {result}"
        save: enhanced_result

# Output formatting
outputs:
  response: "{result}"
  confidence: "{result.confidence}"
  metadata:
    processing_time: "{execution_time}"
    model_used: "{model}"
@end

Provider Configuration

interface ProviderConfig {
  // Traditional ML
  scikitLearn?: ProviderSettings;
  xgboost?: ProviderSettings;
  lightgbm?: ProviderSettings;
  tensorflow?: ProviderSettings;
  
  // Computer Vision
  yolo?: ProviderSettings;
  imageClassification?: ProviderSettings;
  imageSegmentation?: ProviderSettings;
  visionTransformer?: ProviderSettings;
  
  // Audio Processing
  whisper?: ProviderSettings;
  audioEnhancement?: ProviderSettings;
  realTimeAudio?: ProviderSettings;
  speakerEmotion?: ProviderSettings;
  
  // Language Models
  openai?: ProviderSettings;
  anthropic?: ProviderSettings;
  gemini?: ProviderSettings;
  ollama?: ProviderSettings;
}

interface ProviderSettings {
  enabled: boolean;
  priority?: number;
  config?: Record<string, any>;
  timeout?: number;
  retries?: number;
}

Security Configuration

interface SecurityConfig {
  auth: {
    enableAuth: boolean;
    jwtSecret: string;
    apiKeys: string[];
    tokenExpiry: string;
  };
  cors: {
    origin: string[];
    credentials: boolean;
    methods: string[];
  };
  rateLimit: {
    windowMs: number;
    max: number;
  };
  encryption: {
    algorithm: string;
    key: string;
  };
  audit: {
    enabled: boolean;
    logLevel: string;
  };
}

Deployment

Docker Deployment

FROM node:18-alpine

WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

COPY . .
RUN npm run build

EXPOSE 5000
CMD ["npm", "start"]

# Build and run
docker build -t aaabuilder .
docker run -p 5000:5000 \
  -e OPENAI_API_KEY=your-key \
  -e NODE_ENV=production \
  aaabuilder

Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: aaabuilder
spec:
  replicas: 3
  selector:
    matchLabels:
      app: aaabuilder
  template:
    metadata:
      labels:
        app: aaabuilder
    spec:
      containers:
      - name: aaabuilder
        image: aaabuilder:latest
        ports:
        - containerPort: 5000
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: openai

Environment Variables

| Variable | Description | Required | Default | |----------|-------------|----------|---------| | OPENAI_API_KEY | OpenAI API key | Yes | - | | ANTHROPIC_API_KEY | Anthropic API key | No | - | | GEMINI_API_KEY | Google Gemini API key | No | - | | NODE_ENV | Environment | No | development | | PORT | Server port | No | 5000 | | JWT_SECRET | JWT signing secret | No | random | | LOG_LEVEL | Logging level | No | info | | CORS_ORIGIN | CORS origins | No | * |

Contributing

We welcome contributions from the community! Please read our contributing guidelines before submitting pull requests.

Development Setup

# Fork and clone the repository
git clone https://github.com/your-username/AAABuilder.git
cd AAABuilder

# Install dependencies
npm install

# Run tests
npm test

# Start development server
npm run dev

# Run playground
npm run playground

Code Style

• Use TypeScript for all new code
• Follow ESLint and Prettier configurations
• Write comprehensive tests
• Update documentation for new features
• Use conventional commit messages

Testing

# Run all tests
npm test

# Run tests in watch mode
npm run test:watch

# Run specific test suites
npm test -- --testNamePattern="Provider"
npm test -- --testPathPattern="debug"

# Generate coverage report
npm run test:coverage

Pull Request Process

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests if applicable
5. Update documentation
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

License Summary

• License: Apache License 2.0
• Commercial Use: ✓ Allowed
• Modification: ✓ Allowed
• Distribution: ✓ Allowed
• Patent Use: ✓ Allowed
• Private Use: ✓ Allowed
• Liability: ✗ Limited
• Warranty: ✗ Limited

For more information about the Apache License 2.0, visit https://www.apache.org/licenses/LICENSE-2.0.

AAABuilder - Transforming AI/ML development with comprehensive, production-ready solutions.

Built with ❤️ by INIT-ABDELLM

What's Next?

• Model Marketplace: Browse and install pre-trained models
• Workflow Designer: Visual drag-and-drop agent builder
• Analytics Dashboard: Real-time performance monitoring
• Multi-language Support: Python, Rust, and Go bindings
• Cloud Integration: AWS, GCP, Azure native support
• AutoML: Automated model selection and hyperparameter tuning

Stay tuned for updates!