semvec/pypi-packages huggingface dataset:
abstractcontext 0.1.0
Redefining active token memory design for GenAI systems
License: MIT
Requires Python: >=3.8
Keywords: ai, genai, memory, context, tokens, llm

# AbstractContext

> Redefining Active Token Memory Design for GenAI Systems

## Overview

AbstractContext is a foundational Python package that reimagines how generative AI systems manage context and memory. Instead of treating tokens as static units, we propose dynamic, intelligent approaches to context management that optimize both performance and capability.

## Vision

Current GenAI systems face fundamental limitations in how they handle context windows and token allocation. AbstractContext addresses these challenges through:

- **Dynamic Context Management**: Adaptive context windows that grow and shrink based on content relevance
- **Intelligent Token Prioritization**: Smart allocation strategies that preserve critical information while optimizing memory usage
- **Memory-Efficient Compression**: Advanced algorithms for context compression without information loss
- **Adaptive Attention Mechanisms**: Context-aware attention patterns that focus on what matters most

## Status

🚧 **Pre-Alpha Development** - This package is in early conceptual development. The current release provides foundational abstractions and placeholder implementations.

## Installation

```bash
pip install abstractcontext
```

## Quick Start

```python
import abstractcontext

# Get current version
print(abstractcontext.get_version())

# Future usage will include:
# context_manager = abstractcontext.ContextManager()
# token_allocator = abstractcontext.TokenAllocator()
# memory_compressor = abstractcontext.MemoryCompressor()
```

## Core Concepts

### Active Token Memory
Traditional approaches treat all tokens equally. AbstractContext introduces the concept of "active" vs "passive" tokens, where active tokens receive priority in attention and memory allocation.

### Context Relevance Scoring
Dynamic assessment of context segments to determine their relevance to current processing needs, enabling intelligent pruning and compression.

### Adaptive Memory Patterns
Memory allocation strategies that adapt to content type, processing stage, and available resources.

## Development

This package is in active research and development. We welcome contributions from researchers and practitioners working on context management, memory optimization, and GenAI system design.

## License

MIT License - see LICENSE file for details.

## About

AbstractContext is part of the [AbstractCore.ai](https://abstractcore.ai) ecosystem, focused on advancing the foundations of AI system design and memory management.

## Contact

- Organization: [@abstractcore.ai](https://abstractcore.ai)
- Email: contact@abstractcore.ai
- GitHub: https://github.com/lpalbou/abstractcore
- Author: Laurent-Philippe Albou