AAABuilder - Advanced AI/ML Agent Framework

AAABuilder (Agent as a backend) 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.

UNIFIED Idea

The goal of this project is to transform backend processes into fully autonomous AI agents, removing the complexity and overhead of traditional coding. By simply defining an [Example.agent] configuration file, you empower the system to:

• Execute complex, multi-step workflows automatically
• Handle debugging and error resolution with AI assistance
• Leverage pre-built, customizable templates for common AI and ML use cases
• Perform system health checks and monitoring
• Seamlessly integrate and orchestrate various AI/ML models (LLM, vision, GAN, RL, etc.)
• Enable rapid development and deployment of AI-driven backend services without deep coding expertise

This approach dramatically reduces development time, lowers technical barriers, and unlocks new possibilities for building intelligent, adaptable backend systems.

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.

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

Built with ❤️ by INIT-ABDELLM

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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!