pamiq-core


Namepamiq-core JSON
Version 0.5.2 PyPI version JSON
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SummaryFramework for building AI agents with real-time adaptive learning capabilities.
upload_time2025-07-08 23:30:32
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docs_urlNone
authorNone
requires_python>=3.12
licenseMIT License Copyright (c) 2024 MLShukai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords ai ami autonomous machine intelligence ml machine learning p-ami<q> pamiq
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            ![logo](./docs/images/logo.svg)

# pamiq-core

[![PyPI version](https://img.shields.io/pypi/v/pamiq-core.svg)](https://pypi.org/project/pamiq-core/)
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**pamiq-core** is a framework for building AI agents. Developed for P-AMI\<Q>, it enables train and inference in parallel, allowing agents to adapt continuously during interaction with their environment.

[**๐Ÿ“š Documentation site is here.**](https://mlshukai.github.io/pamiq-core/)

## โœจ Features

- ๐Ÿ”„ **Parallel Architecture**: Simultaneous inference and training in separate threads
- โšก **Real-time Adaptation**: Continuously update models during interaction
- ๐Ÿงต **Thread-safe Design**: Robust synchronization mechanisms for parameter sharing and data transfers
- ๐Ÿ”Œ **Modular Components**: Easy-to-extend agent, environment, and model interfaces
- ๐Ÿ› ๏ธ **Comprehensive Tools**: Built-in state persistence, time control, and monitoring
- ๐Ÿ‹๏ธ **Gymnasium Integration**: Seamless compatibility with [Gymnasium](https://gymnasium.farama.org/) environments
- ๐ŸŒ **Cross Platform**: Linux is the primary focus, but Windows and macOS are also supported. (However, some older macOS and Windows systems may have significantly less accurate time control.)

## ๐Ÿ“‹ Requirements

- Python 3.12+
- PyTorch (optional, for torch integration)

## ๐Ÿš€ Quick Start

### Installation

```bash
# Install with pip
pip install pamiq-core

# Optional PyTorch integration
pip install pamiq-core[torch]

# Optional Gymnasium integration
pip install pamiq-core[gym]
```

### Basic Example

```python
from pamiq_core import launch, Interaction, LaunchConfig
from your_agent import YourAgent
from your_environment import YourEnvironment

# Create agent-environment interaction
interaction = Interaction(YourAgent(), YourEnvironment())

# Launch the system
launch(
    interaction=interaction,
    models=your_models,
    buffers=your_data_buffers,
    trainers=your_trainers,
    config=LaunchConfig(
        web_api_address=("localhost", 8391),
        max_uptime=300.0,  # 5 minutes
    ),
)
```

See the [samples](samples/) directory for complete examples.

### Remote CLI Control

Once the system is running, you can connect and control it remotely via the terminal using `pamiq-console`:

```bash
# Connect to local system
pamiq-console --host localhost --port 8391

# Connect to remote system
pamiq-console --host 192.168.1.100 --port 8391
```

## ๐Ÿงฉ Architecture

![PAMIQ System Architecture](docs/images/system-architecture.svg)

pamiq-core implements a unique architecture that enables autonomous intelligence:

1. **Concurrent Threads**: Separate threads for control, inference, and training
2. **Parameter Sharing**: Thread-safe model parameter synchronization
3. **Experience Collection**: Automatic buffering of data from environments, such as images and audio.
4. **Continuous Learning**: Training models while simultaneously using them for decision making
5. **State Persistence**: Saving and loading system state for resumable operation

## ๐Ÿค Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to pamiq-core.

## ๐Ÿ“„ License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## ๐Ÿ”— Related Projects

- [pamiq-recorder](https://github.com/MLShukai/pamiq-recorder): Recording library for P-AMI\<Q>
- [pamiq-io](https://github.com/MLShukai/pamiq-io): I/O library for P-AMI\<Q>

            

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