# VisionAgent - Professional Multi-Modal AI Agent Framework
A cutting-edge, production-ready AI agent platform for image, video, and face analytics built with modern Python and state-of-the-art AI models.
## 🚀 Features
### Core Capabilities
- **Face Detection & Recognition** - Advanced face detection, encoding, and recognition with facial landmarks
- **Object Detection** - YOLOv8-powered object detection with real-time inference
- **Video Analysis** - Frame-by-frame video processing with object/face tracking
- **Image Classification** - HuggingFace Transformers integration for image classification
- **Real-time Processing** - WebSocket streaming for live video analytics
### Technical Excellence
- **Modular Architecture** - Easily extendable agent framework
- **GPU Acceleration** - Automatic CUDA detection with CPU fallback
- **Async Processing** - FastAPI with async endpoints for high performance
- **Production Ready** - Docker support, logging, metrics, and error handling
- **Type Safety** - Full type hints and Pydantic models
- **Scalable** - Batch processing and concurrent request handling
## 🏗️ Architecture
```txt
vision-sphere/
├── agents/ # AI Agent implementations
│ ├── base_agent.py # Abstract base class
│ ├── face_agent.py # Face detection & recognition
│ ├── object_agent.py # Object detection (YOLOv8)
│ ├── video_agent.py # Video analysis & tracking
│ └── classification_agent.py # Image classification
├── models/ # Downloaded/trained models
├── utils/ # Common utilities
│ └── helpers.py # Helper functions
├── server.py # FastAPI application
├── config.py # Configuration management
├── cli.py # Command-line interface
├── requirements.txt # Python dependencies
└── Dockerfile # Container deployment
```
## 🛠️ Installation
### Prerequisites
- Python 3.11+
- CUDA 11.8+ (optional, for GPU acceleration)
- 8GB+ RAM (16GB+ recommended for video processing)
### Quick Start
1. **Clone and Setup**
```bash
git clone <repository-url>
cd vision-sphere
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
```
2. **Run API Server**
```bash
python server.py
```
3. **Access API Documentation**
- Open [http://localhost:8000/docs](http://localhost:8000/docs) for interactive API docs
- Or [http://localhost:8000/redoc](http://localhost:8000/redoc) for alternative documentation
### Docker Deployment
```bash
# Build image
docker build -t visionagent .
# Run with GPU support
docker run --gpus all -p 8000:8000 visionagent
# Run CPU-only
docker run -p 8000:8000 visionagent
```
## 📖 Usage
### Command Line Interface
```bash
# Face detection
python cli.py face image.jpg --output results.json
# Object detection
python cli.py object image.jpg --confidence 0.7 --verbose
# Video analysis
python cli.py video video.mp4 --max-frames 500 --format detailed
# Image classification
python cli.py classify image.jpg --top-k 10 --confidence 0.1
# System information
python cli.py info
# Start server
python cli.py server --host 0.0.0.0 --port 8000
```
### API Endpoints
#### Face Detection
```bash
# Upload file
curl -X POST "http://localhost:8000/face" \
-F "file=@image.jpg"
# Or use image URL
curl -X POST "http://localhost:8000/face" \
-H "Content-Type: application/json" \
-d '{"image_url": "https://example.com/image.jpg"}'
```
#### Object Detection
```bash
curl -X POST "http://localhost:8000/object" \
-F "file=@image.jpg"
```
#### Video Analysis
```bash
curl -X POST "http://localhost:8000/video" \
-F "file=@video.mp4"
```
#### Image Classification
```bash
curl -X POST "http://localhost:8000/classify" \
-F "file=@image.jpg"
```
#### Batch Processing
```bash
curl -X POST "http://localhost:8000/batch/classify" \
-F "files=@image1.jpg" \
-F "files=@image2.jpg" \
-F "files=@image3.jpg"
```
### WebSocket Streaming
```javascript
// Real-time video processing
const ws = new WebSocket('ws://localhost:8000/ws/video');
ws.onopen = function() {
// Send video frames as binary data
ws.send(frameData);
};
ws.onmessage = function(event) {
const result = JSON.parse(event.data);
console.log('Analysis result:', result);
};
```
## ⚙️ Configuration
Create a `config.yaml` file to customize the framework:
```yaml
# Global settings
default_device: "auto" # auto, cpu, cuda
model_cache_dir: "./models"
temp_dir: "./temp"
# Face Agent
face_agent:
enabled: true
model:
name: "face_recognition"
confidence_threshold: 0.6
custom_params:
face_detection_model: "hog" # hog, cnn
num_jitters: 1
tolerance: 0.6
# Object Agent
object_agent:
enabled: true
model:
name: "yolov8s.pt"
confidence_threshold: 0.5
custom_params:
iou_threshold: 0.45
max_detections: 100
# Video Agent
video_agent:
enabled: true
processing_params:
frame_skip: 1
max_frames: 1000
track_objects: true
track_faces: true
# Classification Agent
classification_agent:
enabled: true
model:
name: "microsoft/resnet-50"
custom_params:
top_k: 5
threshold: 0.1
return_features: false
# Server Configuration
server:
host: "0.0.0.0"
port: 8000
workers: 1
max_file_size_mb: 100
enable_websocket: true
rate_limit_per_minute: 60
# Logging
logging:
level: "INFO"
file_path: "./logs/visionagent.log"
max_file_size_mb: 10
backup_count: 5
```
### Environment Variables
```bash
# Override configuration with environment variables
export VISIONAGENT_CONFIG=/path/to/config.yaml
export VISIONAGENT_DEVICE=cuda
export VISIONAGENT_HOST=0.0.0.0
export VISIONAGENT_PORT=8000
export VISIONAGENT_LOG_LEVEL=DEBUG
export VISIONAGENT_MODEL_CACHE_DIR=/app/models
```
## 🧩 Extending the Framework
### Creating Custom Agents
```python
from agents.base_agent import BaseAgent, ProcessingResult
class CustomAgent(BaseAgent):
def initialize(self) -> bool:
# Initialize your model here
self._is_initialized = True
return True
def process(self, input_data: Any) -> ProcessingResult:
# Implement your processing logic
try:
# Your processing code here
result_data = {"custom_analysis": "results"}
return ProcessingResult(
success=True,
data=result_data,
confidence=0.95,
inference_time=50.0
)
except Exception as e:
return ProcessingResult(
success=False,
data={},
error=str(e)
)
```
## 📊 API Response Format
All endpoints return standardized responses:
```json
{
"success": true,
"data": {
"detections": [...],
"detection_count": 5,
"class_summary": {...}
},
"inference_time_ms": 45.2,
"agent_info": {
"agent_type": "ObjectAgent",
"device": "cuda",
"initialized": true
},
"timestamp": "2025-08-31T12:00:00.000Z",
"request_id": "uuid-string"
}
```
## 🔧 Development
### Setup Development Environment
```bash
# Install development dependencies
pip install -r requirements.txt
pip install pytest pytest-asyncio black flake8 mypy
# Run tests
pytest
# Format code
black .
# Lint code
flake8 .
mypy .
```
### Project Structure Guidelines
- **agents/** - All AI agent implementations inherit from `BaseAgent`
- **models/** - Downloaded model files and weights
- **utils/** - Shared utilities and helper functions
- **server.py** - FastAPI application with all endpoints
- **config.py** - Centralized configuration management
- **cli.py** - Command-line interface for all agents
## 🚀 Production Deployment
sDocker Deployment
```bash
# Build production image
docker build -t visionagent:latest .
# Run with GPU support
docker run --gpus all \
-p 8000:8000 \
-v $(pwd)/models:/app/models \
-v $(pwd)/logs:/app/logs \
-e VISIONAGENT_LOG_LEVEL=INFO \
visionagent:latest
# Docker Compose (recommended)
docker-compose up -d
```
### Kubernetes Deployment
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: visionagent
spec:
replicas: 3
selector:
matchLabels:
app: visionagent
template:
metadata:
labels:
app: visionagent
spec:
containers:
- name: visionagent
image: visionagent:latest
ports:
- containerPort: 8000
env:
- name: VISIONAGENT_DEVICE
value: "cuda"
resources:
requests:
nvidia.com/gpu: 1
limits:
nvidia.com/gpu: 1
```
## 📈 Performance Optimization
### GPU Acceleration
- Automatic CUDA detection and device selection
- Batch processing for multiple images
- Memory-efficient model loading
### Scalability Features
- Async FastAPI endpoints
- WebSocket streaming for real-time processing
- Configurable worker processes
- Model caching and lazy loading
## 🔒 Security Considerations
- File size limits for uploads
- Input validation and sanitization
- Non-root container execution
- Rate limiting support
- CORS configuration
## 🧪 Testing
```bash
# Run all tests
pytest
# Run specific test categories
pytest tests/test_agents.py
pytest tests/test_api.py
pytest tests/test_utils.py
# Run with coverage
pytest --cov=agents --cov=utils --cov-report=html
```
## 📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## 🆘 Support
For issues and questions:
- Check the [API documentation](http://localhost:8000/docs)
- Review the [configuration guide]([def]: #configuration)
- Check system requirements and GPU setup
- Enable debug logging for detailed error information
## 🎯 Roadmap
- [ ] ONNX model support for cross-platform deployment
- [ ] Advanced video tracking algorithms
- [ ] Real-time face recognition optimization
- [ ] Model quantization for edge deployment
- [ ] Multi-camera support
- [ ] Advanced analytics and reporting
- [ ] Model fine-tuning utilities
- [ ] REST API rate limiting and authentication
Raw data
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"name": "vision-agent-framework",
"maintainer": "Krishna Bajpai, Vedanshi Gupta",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "krishna.bajpai@example.com",
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"author": "Krishna Bajpai, Vedanshi Gupta",
"author_email": "krishna@krishnabajpai.me, vedanshigupta158@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/20/6f/f116ba4cd2d27699b70a94d7a9aa23c834188dac64751fae0b6cd35f0a16/vision_agent_framework-1.0.0.tar.gz",
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"description": "# VisionAgent - Professional Multi-Modal AI Agent Framework\r\n\r\nA cutting-edge, production-ready AI agent platform for image, video, and face analytics built with modern Python and state-of-the-art AI models.\r\n\r\n## \ud83d\ude80 Features\r\n\r\n### Core Capabilities\r\n\r\n- **Face Detection & Recognition** - Advanced face detection, encoding, and recognition with facial landmarks\r\n- **Object Detection** - YOLOv8-powered object detection with real-time inference\r\n- **Video Analysis** - Frame-by-frame video processing with object/face tracking\r\n- **Image Classification** - HuggingFace Transformers integration for image classification\r\n- **Real-time Processing** - WebSocket streaming for live video analytics\r\n\r\n### Technical Excellence\r\n\r\n- **Modular Architecture** - Easily extendable agent framework\r\n- **GPU Acceleration** - Automatic CUDA detection with CPU fallback\r\n- **Async Processing** - FastAPI with async endpoints for high performance\r\n- **Production Ready** - Docker support, logging, metrics, and error handling\r\n- **Type Safety** - Full type hints and Pydantic models\r\n- **Scalable** - Batch processing and concurrent request handling\r\n\r\n## \ud83c\udfd7\ufe0f Architecture\r\n\r\n```txt\r\nvision-sphere/\r\n\u251c\u2500\u2500 agents/ # AI Agent implementations\r\n\u2502 \u251c\u2500\u2500 base_agent.py # Abstract base class\r\n\u2502 \u251c\u2500\u2500 face_agent.py # Face detection & recognition\r\n\u2502 \u251c\u2500\u2500 object_agent.py # Object detection (YOLOv8)\r\n\u2502 \u251c\u2500\u2500 video_agent.py # Video analysis & tracking\r\n\u2502 \u2514\u2500\u2500 classification_agent.py # Image classification\r\n\u251c\u2500\u2500 models/ # Downloaded/trained models\r\n\u251c\u2500\u2500 utils/ # Common utilities\r\n\u2502 \u2514\u2500\u2500 helpers.py # Helper functions\r\n\u251c\u2500\u2500 server.py # FastAPI application\r\n\u251c\u2500\u2500 config.py # Configuration management\r\n\u251c\u2500\u2500 cli.py # Command-line interface\r\n\u251c\u2500\u2500 requirements.txt # Python dependencies\r\n\u2514\u2500\u2500 Dockerfile # Container deployment\r\n```\r\n\r\n## \ud83d\udee0\ufe0f Installation\r\n\r\n### Prerequisites\r\n\r\n- Python 3.11+\r\n- CUDA 11.8+ (optional, for GPU acceleration)\r\n- 8GB+ RAM (16GB+ recommended for video processing)\r\n\r\n### Quick Start\r\n\r\n1. **Clone and Setup**\r\n\r\n ```bash\r\n git clone <repository-url>\r\n cd vision-sphere\r\n python -m venv venv\r\n source venv/bin/activate # On Windows: venv\\Scripts\\activate\r\n pip install -r requirements.txt\r\n ```\r\n\r\n2. **Run API Server**\r\n\r\n ```bash\r\n python server.py\r\n ```\r\n\r\n3. **Access API Documentation**\r\n\r\n - Open [http://localhost:8000/docs](http://localhost:8000/docs) for interactive API docs\r\n - Or [http://localhost:8000/redoc](http://localhost:8000/redoc) for alternative documentation\r\n\r\n### Docker Deployment\r\n\r\n```bash\r\n# Build image\r\ndocker build -t visionagent .\r\n\r\n# Run with GPU support\r\ndocker run --gpus all -p 8000:8000 visionagent\r\n\r\n# Run CPU-only\r\ndocker run -p 8000:8000 visionagent\r\n```\r\n\r\n## \ud83d\udcd6 Usage\r\n\r\n### Command Line Interface\r\n\r\n```bash\r\n# Face detection\r\npython cli.py face image.jpg --output results.json\r\n\r\n# Object detection \r\npython cli.py object image.jpg --confidence 0.7 --verbose\r\n\r\n# Video analysis\r\npython cli.py video video.mp4 --max-frames 500 --format detailed\r\n\r\n# Image classification\r\npython cli.py classify image.jpg --top-k 10 --confidence 0.1\r\n\r\n# System information\r\npython cli.py info\r\n\r\n# Start server\r\npython cli.py server --host 0.0.0.0 --port 8000\r\n```\r\n\r\n### API Endpoints\r\n\r\n#### Face Detection\r\n\r\n```bash\r\n# Upload file\r\ncurl -X POST \"http://localhost:8000/face\" \\\r\n -F \"file=@image.jpg\"\r\n\r\n# Or use image URL\r\ncurl -X POST \"http://localhost:8000/face\" \\\r\n -H \"Content-Type: application/json\" \\\r\n -d '{\"image_url\": \"https://example.com/image.jpg\"}'\r\n```\r\n\r\n#### Object Detection\r\n\r\n```bash\r\ncurl -X POST \"http://localhost:8000/object\" \\\r\n -F \"file=@image.jpg\"\r\n```\r\n\r\n#### Video Analysis\r\n\r\n```bash\r\ncurl -X POST \"http://localhost:8000/video\" \\\r\n -F \"file=@video.mp4\"\r\n```\r\n\r\n#### Image Classification\r\n\r\n```bash\r\ncurl -X POST \"http://localhost:8000/classify\" \\\r\n -F \"file=@image.jpg\"\r\n```\r\n\r\n#### Batch Processing\r\n\r\n```bash\r\ncurl -X POST \"http://localhost:8000/batch/classify\" \\\r\n -F \"files=@image1.jpg\" \\\r\n -F \"files=@image2.jpg\" \\\r\n -F \"files=@image3.jpg\"\r\n```\r\n\r\n### WebSocket Streaming\r\n\r\n```javascript\r\n// Real-time video processing\r\nconst ws = new WebSocket('ws://localhost:8000/ws/video');\r\n\r\nws.onopen = function() {\r\n // Send video frames as binary data\r\n ws.send(frameData);\r\n};\r\n\r\nws.onmessage = function(event) {\r\n const result = JSON.parse(event.data);\r\n console.log('Analysis result:', result);\r\n};\r\n```\r\n\r\n## \u2699\ufe0f Configuration\r\n\r\nCreate a `config.yaml` file to customize the framework:\r\n\r\n```yaml\r\n# Global settings\r\ndefault_device: \"auto\" # auto, cpu, cuda\r\nmodel_cache_dir: \"./models\"\r\ntemp_dir: \"./temp\"\r\n\r\n# Face Agent\r\nface_agent:\r\n enabled: true\r\n model:\r\n name: \"face_recognition\"\r\n confidence_threshold: 0.6\r\n custom_params:\r\n face_detection_model: \"hog\" # hog, cnn\r\n num_jitters: 1\r\n tolerance: 0.6\r\n\r\n# Object Agent\r\nobject_agent:\r\n enabled: true\r\n model:\r\n name: \"yolov8s.pt\"\r\n confidence_threshold: 0.5\r\n custom_params:\r\n iou_threshold: 0.45\r\n max_detections: 100\r\n\r\n# Video Agent\r\nvideo_agent:\r\n enabled: true\r\n processing_params:\r\n frame_skip: 1\r\n max_frames: 1000\r\n track_objects: true\r\n track_faces: true\r\n\r\n# Classification Agent\r\nclassification_agent:\r\n enabled: true\r\n model:\r\n name: \"microsoft/resnet-50\"\r\n custom_params:\r\n top_k: 5\r\n threshold: 0.1\r\n return_features: false\r\n\r\n# Server Configuration\r\nserver:\r\n host: \"0.0.0.0\"\r\n port: 8000\r\n workers: 1\r\n max_file_size_mb: 100\r\n enable_websocket: true\r\n rate_limit_per_minute: 60\r\n\r\n# Logging\r\nlogging:\r\n level: \"INFO\"\r\n file_path: \"./logs/visionagent.log\"\r\n max_file_size_mb: 10\r\n backup_count: 5\r\n```\r\n\r\n### Environment Variables\r\n\r\n```bash\r\n# Override configuration with environment variables\r\nexport VISIONAGENT_CONFIG=/path/to/config.yaml\r\nexport VISIONAGENT_DEVICE=cuda\r\nexport VISIONAGENT_HOST=0.0.0.0\r\nexport VISIONAGENT_PORT=8000\r\nexport VISIONAGENT_LOG_LEVEL=DEBUG\r\nexport VISIONAGENT_MODEL_CACHE_DIR=/app/models\r\n```\r\n\r\n## \ud83e\udde9 Extending the Framework\r\n\r\n### Creating Custom Agents\r\n\r\n```python\r\nfrom agents.base_agent import BaseAgent, ProcessingResult\r\n\r\nclass CustomAgent(BaseAgent):\r\n def initialize(self) -> bool:\r\n # Initialize your model here\r\n self._is_initialized = True\r\n return True\r\n \r\n def process(self, input_data: Any) -> ProcessingResult:\r\n # Implement your processing logic\r\n try:\r\n # Your processing code here\r\n result_data = {\"custom_analysis\": \"results\"}\r\n \r\n return ProcessingResult(\r\n success=True,\r\n data=result_data,\r\n confidence=0.95,\r\n inference_time=50.0\r\n )\r\n except Exception as e:\r\n return ProcessingResult(\r\n success=False,\r\n data={},\r\n error=str(e)\r\n )\r\n```\r\n\r\n## \ud83d\udcca API Response Format\r\n\r\nAll endpoints return standardized responses:\r\n\r\n```json\r\n{\r\n \"success\": true,\r\n \"data\": {\r\n \"detections\": [...],\r\n \"detection_count\": 5,\r\n \"class_summary\": {...}\r\n },\r\n \"inference_time_ms\": 45.2,\r\n \"agent_info\": {\r\n \"agent_type\": \"ObjectAgent\",\r\n \"device\": \"cuda\",\r\n \"initialized\": true\r\n },\r\n \"timestamp\": \"2025-08-31T12:00:00.000Z\",\r\n \"request_id\": \"uuid-string\"\r\n}\r\n```\r\n\r\n## \ud83d\udd27 Development\r\n\r\n### Setup Development Environment\r\n\r\n```bash\r\n# Install development dependencies\r\npip install -r requirements.txt\r\npip install pytest pytest-asyncio black flake8 mypy\r\n\r\n# Run tests\r\npytest\r\n\r\n# Format code\r\nblack .\r\n\r\n# Lint code\r\nflake8 .\r\nmypy .\r\n```\r\n\r\n### Project Structure Guidelines\r\n\r\n- **agents/** - All AI agent implementations inherit from `BaseAgent`\r\n- **models/** - Downloaded model files and weights\r\n- **utils/** - Shared utilities and helper functions\r\n- **server.py** - FastAPI application with all endpoints\r\n- **config.py** - Centralized configuration management\r\n- **cli.py** - Command-line interface for all agents\r\n\r\n## \ud83d\ude80 Production Deployment\r\n\r\nsDocker Deployment\r\n\r\n```bash\r\n# Build production image\r\ndocker build -t visionagent:latest .\r\n\r\n# Run with GPU support\r\ndocker run --gpus all \\\r\n -p 8000:8000 \\\r\n -v $(pwd)/models:/app/models \\\r\n -v $(pwd)/logs:/app/logs \\\r\n -e VISIONAGENT_LOG_LEVEL=INFO \\\r\n visionagent:latest\r\n\r\n# Docker Compose (recommended)\r\ndocker-compose up -d\r\n```\r\n\r\n### Kubernetes Deployment\r\n\r\n```yaml\r\napiVersion: apps/v1\r\nkind: Deployment\r\nmetadata:\r\n name: visionagent\r\nspec:\r\n replicas: 3\r\n selector:\r\n matchLabels:\r\n app: visionagent\r\n template:\r\n metadata:\r\n labels:\r\n app: visionagent\r\n spec:\r\n containers:\r\n - name: visionagent\r\n image: visionagent:latest\r\n ports:\r\n - containerPort: 8000\r\n env:\r\n - name: VISIONAGENT_DEVICE\r\n value: \"cuda\"\r\n resources:\r\n requests:\r\n nvidia.com/gpu: 1\r\n limits:\r\n nvidia.com/gpu: 1\r\n```\r\n\r\n## \ud83d\udcc8 Performance Optimization\r\n\r\n### GPU Acceleration\r\n\r\n- Automatic CUDA detection and device selection\r\n- Batch processing for multiple images\r\n- Memory-efficient model loading\r\n\r\n### Scalability Features\r\n\r\n- Async FastAPI endpoints\r\n- WebSocket streaming for real-time processing\r\n- Configurable worker processes\r\n- Model caching and lazy loading\r\n\r\n## \ud83d\udd12 Security Considerations\r\n\r\n- File size limits for uploads\r\n- Input validation and sanitization\r\n- Non-root container execution\r\n- Rate limiting support\r\n- CORS configuration\r\n\r\n## \ud83e\uddea Testing\r\n\r\n```bash\r\n# Run all tests\r\npytest\r\n\r\n# Run specific test categories\r\npytest tests/test_agents.py\r\npytest tests/test_api.py\r\npytest tests/test_utils.py\r\n\r\n# Run with coverage\r\npytest --cov=agents --cov=utils --cov-report=html\r\n```\r\n\r\n## \ud83d\udcdd License\r\n\r\nThis project is licensed under the MIT License - see the LICENSE file for details.\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\n1. Fork the repository\r\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\r\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\r\n4. Push to the branch (`git push origin feature/amazing-feature`)\r\n5. Open a Pull Request\r\n\r\n## \ud83c\udd98 Support\r\n\r\nFor issues and questions:\r\n\r\n- Check the [API documentation](http://localhost:8000/docs)\r\n- Review the [configuration guide]([def]: #configuration)\r\n- Check system requirements and GPU setup\r\n- Enable debug logging for detailed error information\r\n\r\n## \ud83c\udfaf Roadmap\r\n\r\n- [ ] ONNX model support for cross-platform deployment\r\n- [ ] Advanced video tracking algorithms\r\n- [ ] Real-time face recognition optimization\r\n- [ ] Model quantization for edge deployment\r\n- [ ] Multi-camera support\r\n- [ ] Advanced analytics and reporting\r\n- [ ] Model fine-tuning utilities\r\n- [ ] REST API rate limiting and authentication\r\n",
"bugtrack_url": null,
"license": null,
"summary": "World-Class Multi-Modal AI Agent Framework with Revolutionary Performance Features",
"version": "1.0.0",
"project_urls": {
"Bug Reports": "https://github.com/krishna-bajpai/vision-agent/issues",
"Changelog": "https://github.com/krishna-bajpai/vision-agent/blob/main/CHANGELOG.md",
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