Metadata-Version: 2.4
Name: AbstractMemory
Version: 0.0.1
Summary: PLACEHOLDER: Memory system for transforming stateless LLMs into stateful LLMs - primarily designed for AbstractLLM integration
Author-email: AbstractMemory Team <contact@example.com>
Maintainer-email: AbstractMemory Team <contact@example.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/abstractmemory/abstractmemory
Project-URL: Documentation, https://github.com/abstractmemory/abstractmemory#readme
Project-URL: Repository, https://github.com/abstractmemory/abstractmemory
Project-URL: Bug Reports, https://github.com/abstractmemory/abstractmemory/issues
Keywords: llm,memory,stateful,ai,placeholder
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# AbstractMemory - PLACEHOLDER PROJECT

⚠️ **WARNING: This is a placeholder package** ⚠️

## Overview

AbstractMemory is a placeholder package that reserves the name on PyPI for a future memory system designed to transform stateless LLMs into stateful LLMs.

## Current Status

**This package is currently a PLACEHOLDER and should NOT be used in production.**

The actual memory system implementation is currently integrated within the AbstractLLM project. This separate package exists to:

1. **Reserve the PyPI name** for future modularization
2. **Enable clean separation of concerns** when the code is extracted from AbstractLLM
3. **Facilitate better evolution and maintenance** of the memory system as a standalone component
4. **Allow reusability** across different LLM frameworks in the future

## Future Vision

AbstractMemory will provide:

- **Stateful Memory Management**: Transform stateless LLMs into stateful systems
- **Primary AbstractLLM Integration**: Seamless integration with AbstractLLM
- **Modular Architecture**: Clean separation from core LLM functionality
- **Extensible Framework**: Support for various memory strategies and backends

## Installation

```bash
pip install AbstractMemory
```

## Usage

Currently, attempting to use any functionality will raise a `PlaceholderError`:

```python
import abstractmemory

# This will raise PlaceholderError
abstractmemory.placeholder_warning()
```

## Development Timeline

The actual implementation will be extracted and modularized from AbstractLLM when:
- The AbstractLLM memory system reaches sufficient maturity
- Clean interfaces are established
- Comprehensive testing framework is in place

## Contributing

This is a placeholder project. For memory-related contributions, please refer to the AbstractLLM project until the code is modularized.

## License

MIT License - See LICENSE file for details.

## Contact

For questions about future development plans, please refer to the AbstractLLM project documentation.

---

**Remember: This is a placeholder. The real implementation is coming soon!**
