Name | AbstractMemory JSON |
Version |
0.0.1
JSON |
| download |
home_page | None |
Summary | PLACEHOLDER: Memory system for transforming stateless LLMs into stateful LLMs - primarily designed for AbstractLLM integration |
upload_time | 2025-09-19 10:22:58 |
maintainer | None |
docs_url | None |
author | None |
requires_python | >=3.8 |
license | None |
keywords |
llm
memory
stateful
ai
placeholder
|
VCS |
 |
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
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# 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!**
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