| Name | pamiq-core JSON |
| Version |
0.5.2
JSON |
| download |
| home_page | None |
| Summary | Framework for building AI agents with real-time adaptive learning capabilities. |
| upload_time | 2025-07-08 23:30:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | MIT 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
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
# pamiq-core
[](https://pypi.org/project/pamiq-core/)
[](https://www.python.org/downloads/)
[](LICENSE)
[](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings)
[](https://github.com/MLShukai/pamiq-core/actions/workflows/test.yml)
[](https://github.com/MLShukai/pamiq-core/actions/workflows/type-check.yml)
[](https://github.com/MLShukai/pamiq-core/actions/workflows/pre-commit.yml)
**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.
[**📚 Documentation site is here.**](https://mlshukai.github.io/pamiq-core/)
## ✨ Features
- 🔄 **Parallel Architecture**: Simultaneous inference and training in separate threads
- ⚡ **Real-time Adaptation**: Continuously update models during interaction
- 🧵 **Thread-safe Design**: Robust synchronization mechanisms for parameter sharing and data transfers
- 🔌 **Modular Components**: Easy-to-extend agent, environment, and model interfaces
- 🛠️ **Comprehensive Tools**: Built-in state persistence, time control, and monitoring
- 🏋️ **Gymnasium Integration**: Seamless compatibility with [Gymnasium](https://gymnasium.farama.org/) environments
- 🌍 **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.)
## 📋 Requirements
- Python 3.12+
- PyTorch (optional, for torch integration)
## 🚀 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
```
## 🧩 Architecture
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
## 🤝 Contributing
Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to pamiq-core.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🔗 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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"description": "\n\n# pamiq-core\n\n[](https://pypi.org/project/pamiq-core/)\n[](https://www.python.org/downloads/)\n[](LICENSE)\n[](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings)\n[](https://github.com/MLShukai/pamiq-core/actions/workflows/test.yml)\n[](https://github.com/MLShukai/pamiq-core/actions/workflows/type-check.yml)\n[](https://github.com/MLShukai/pamiq-core/actions/workflows/pre-commit.yml)\n\n**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.\n\n[**\ud83d\udcda Documentation site is here.**](https://mlshukai.github.io/pamiq-core/)\n\n## \u2728 Features\n\n- \ud83d\udd04 **Parallel Architecture**: Simultaneous inference and training in separate threads\n- \u26a1 **Real-time Adaptation**: Continuously update models during interaction\n- \ud83e\uddf5 **Thread-safe Design**: Robust synchronization mechanisms for parameter sharing and data transfers\n- \ud83d\udd0c **Modular Components**: Easy-to-extend agent, environment, and model interfaces\n- \ud83d\udee0\ufe0f **Comprehensive Tools**: Built-in state persistence, time control, and monitoring\n- \ud83c\udfcb\ufe0f **Gymnasium Integration**: Seamless compatibility with [Gymnasium](https://gymnasium.farama.org/) environments\n- \ud83c\udf0d **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.)\n\n## \ud83d\udccb Requirements\n\n- Python 3.12+\n- PyTorch (optional, for torch integration)\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\n# Install with pip\npip install pamiq-core\n\n# Optional PyTorch integration\npip install pamiq-core[torch]\n\n# Optional Gymnasium integration\npip install pamiq-core[gym]\n```\n\n### Basic Example\n\n```python\nfrom pamiq_core import launch, Interaction, LaunchConfig\nfrom your_agent import YourAgent\nfrom your_environment import YourEnvironment\n\n# Create agent-environment interaction\ninteraction = Interaction(YourAgent(), YourEnvironment())\n\n# Launch the system\nlaunch(\n interaction=interaction,\n models=your_models,\n buffers=your_data_buffers,\n trainers=your_trainers,\n config=LaunchConfig(\n web_api_address=(\"localhost\", 8391),\n max_uptime=300.0, # 5 minutes\n ),\n)\n```\n\nSee the [samples](samples/) directory for complete examples.\n\n### Remote CLI Control\n\nOnce the system is running, you can connect and control it remotely via the terminal using `pamiq-console`:\n\n```bash\n# Connect to local system\npamiq-console --host localhost --port 8391\n\n# Connect to remote system\npamiq-console --host 192.168.1.100 --port 8391\n```\n\n## \ud83e\udde9 Architecture\n\n\n\npamiq-core implements a unique architecture that enables autonomous intelligence:\n\n1. **Concurrent Threads**: Separate threads for control, inference, and training\n2. **Parameter Sharing**: Thread-safe model parameter synchronization\n3. **Experience Collection**: Automatic buffering of data from environments, such as images and audio.\n4. **Continuous Learning**: Training models while simultaneously using them for decision making\n5. **State Persistence**: Saving and loading system state for resumable operation\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to contribute to pamiq-core.\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\udd17 Related Projects\n\n- [pamiq-recorder](https://github.com/MLShukai/pamiq-recorder): Recording library for P-AMI\\<Q>\n- [pamiq-io](https://github.com/MLShukai/pamiq-io): I/O library for P-AMI\\<Q>\n",
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