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

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- 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.

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| 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

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- 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

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- 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

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- 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

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- 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

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#### Debugging Framework
``typescript
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
`typescript
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
`bash

Start interactive development environment

npm run playground

Available commands:

load - Load agent file

debug - Start debugging session

test - Run tests

validate - Validate agent

docs - Generate documentation

`

#### Project Scaffolding
`bash

Create new project with interactive wizard

npm run create

Create from template

npm run create:interactive

List available templates

npm run templates
`

#### Auto-Documentation
`bash

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
`

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- 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

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

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`bash

Install globally

npm install -g aaab

Verify installation

aaab --version
`

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`bash

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
`

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`yaml
@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

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`bash
npm install -g aaab
`

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`bash

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
`

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`bash

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

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`bash

Core commands

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

Development tools

aaab debug # Start debugging session
aaab test # Run test suite
aaab playground # Start interactive playground
aaab create # Create new project
aaab docs # Generate documentation

Provider management

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

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`bash

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

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`typescript
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 }
});
`

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`typescript
// 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);
`

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`typescript
// 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

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

`typescript
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);
`

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

`typescript
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
});
`

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Real-time development environment for agent creation and testing.

`bash

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
`

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Interactive project creation with templates and best practices.

`bash

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

`

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Generate comprehensive documentation in multiple formats.

`bash

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

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`yaml
@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
`

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`yaml
@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
`

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`yaml
@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
`

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`yaml
@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

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`yaml
@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
`

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`typescript
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;
timeout?: number;
retries?: number;
}
`

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`typescript
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

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`dockerfile
FROM node:18-alpine

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

COPY . .
RUN npm run build

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

`bash

Build and run

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

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`yaml
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
`

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| 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.

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`bash

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
`

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

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`bash

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
`

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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.

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- 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!