VisionSync is a comprehensive agent-based framework that combines pattern recognition, resource optimization, learning integration, and multi-agent cooperation into a cohesive system. It's designed to grow and adapt through experience, making it ideal for complex applications requiring intelligent automation.
-
Core Agent System
- Hierarchical context management
- Configurable model integration
- State and lifecycle management
- Comprehensive logging and monitoring
-
Pattern Recognition
- Semantic pattern detection
- Pattern learning and adaptation
- Similarity-based matching
- Configurable thresholds
-
Resource Optimization
- Dynamic resource allocation
- Usage monitoring
- Memory/CPU optimization
- Resource allocation strategies
-
Learning Integration
- Experience-based learning
- Knowledge application
- Configurable learning rates
- Memory management
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Multi-Agent Cooperation
- Agent coordination
- Task delegation
- Team management
- Coordination thresholds
-
System Evolution
- Performance evaluation
- System adaptation
- Evolution strategies
- Generation-based improvements
-
Analytics System
- Metric collection
- Trend analysis
- Performance monitoring
- Configurable analysis
-
Interface System
- Input/output formatting
- Markdown support
- Streaming capability
- Style management
VisionSync is currently in pre-release (v0.1.0). You can install it directly from the GitHub repository:
# Clone the repository
git clone https://github.com/ab1355/visionsync.git
cd visionsync
# Install in development mode with all dependencies
pip install -e ".[dev]"- Python >=3.9
- setuptools>=42
- wheel
from visionsync import AgentSystem
from visionsync.systems import PatternEngine
# Initialize the system
agent = AgentSystem()
# Process patterns
async def analyze_patterns(data: dict):
engine = PatternEngine()
result = await agent.process_patterns(
data,
engine=engine,
similarity_threshold=0.75
)
return result
# Use the results
analysis = await analyze_patterns({"text": "example data"})
print(f"Detected patterns: {analysis.patterns}")
print(f"Pattern confidence: {analysis.confidence}")VisionSync is built on a modular architecture with several key components:
- Core Engine: Manages system state and coordination
- Pattern Engine: Handles pattern recognition and learning
- Resource Manager: Optimizes system resources
- Learning System: Adapts and improves from experience
- Cooperation System: Manages multi-agent interactions
- Evolution Engine: Handles system adaptation
- Analytics Engine: Monitors and analyzes performance
- Interface Engine: Manages I/O formatting
VisionSync is under active development (v0.1.0). Current focus areas:
- Enhanced pattern recognition
- Improved resource optimization
- Extended learning capabilities
- Performance optimization
- Documentation expansion
- PyPI package release
- Clone the repository:
git clone https://github.com/ab1355/visionsync.git
cd visionsync- Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install development dependencies:
pip install -e ".[dev]"- Run tests:
pytestWe welcome contributions! Please see our Contributing Guide for details.
VisionSync is released under the MIT License. See the LICENSE file for details.
Special thanks to all our contributors and the open source community.