semvec/pypi-packages huggingface dataset:
optimation 2.2
Enhanced version 2.2 with variable modeling and optimation.
Requires Python: >=3.6

# Optimation v2.2

A flexible framework for variable weighting, quantum-inspired logic, and simplified modeling.

## Features
- Variable Weighting
- Exponential/Quantum Logic
- Modeling: Variables, Objectives, Minimization

## Example

```python
from optimation import Variable, Model, Objective, minimize

x = Variable("x", 10)
y = Variable("y", 20)

def total(vars): return sum(v.value for v in vars)

model = Model()
model.add_variable(x)
model.add_variable(y)
model.add_objective(Objective(total))

print(minimize(model))
```
AgenticLearnPro 0.1.2
A simple agentic AI package using reinforcement learning
Requires Python: >=3.6

# Agentic AI

A simple agentic AI package using reinforcement learning. This package provides a basic implementation of a Q-learning agent that can interact with a simple environment to achieve goals.

## Installation

```bash
pip install AgenticLearnPro
```

## Usage

```python
from AgenticLearnPro.agent import QLearningAgent
from AgenticLearnPro.environment import SimpleEnv

# Create environment and agent
env = SimpleEnv()
agent = QLearningAgent(state_space=env.state_space, action_space=env.action_space)

# Train the agent
for episode in range(100):
    state = env.reset()
    done = False
    while not done:
        action = agent.choose_action(state)
        next_state, reward, done = env.step(action)
        agent.learn(state, action, reward, next_state)
        state = next_state
    agent.decay_exploration()

# Test the trained agent
state = env.reset()
done = False
total_reward = 0
while not done:
    action = agent.choose_action(state)
    next_state, reward, done = env.step(action)
    total_reward += reward
    state = next_state
    print(f"State: {state}, Action: {action}, Reward: {reward}")
print(f"Total reward: {total_reward}")
```

## Features

- Simple Q-learning agent implementation
- Basic environment with states and actions
- Configurable learning parameters
- Exploration rate decay for better convergence

## License

MIT

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
mlop 0.0.2rc5
mlop

<img src="https://github.com/mlop-ai/mlop/raw/refs/heads/main/design/favicon.svg?sanitize=true" alt="logo" height="80">

[![stars](https://img.shields.io/github/stars/mlop-ai/mlop)](https://github.com/mlop-ai/mlop/stargazers)
[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mlop-ai/mlop/blob/main/examples/intro.ipynb)
[![pypi](https://img.shields.io/pypi/v/mlop)](https://pypi.org/project/mlop/)
[![license](https://img.shields.io/github/license/mlop-ai/mlop)](https://github.com/mlop-ai/mlop/blob/main/LICENSE)
<!-- [![build](https://img.shields.io/github/actions/workflow/status/mlop-ai/mlop/mlop.yml)](https://github.com/mlop-ai/mlop/actions/workflows/mlop.yml) -->

**mlop** is a Machine Learning Operations (MLOps) framework. It provides [self-hostable superior experimental tracking capabilities and lifecycle management for training ML models](https://github.com/mlop-ai/server). To get started, [try out our introductory notebook](https://colab.research.google.com/github/mlop-ai/mlop/blob/main/examples/intro.ipynb) or [get an account with us today](https://app.mlop.ai/auth/sign-up)!

## 🎥 Demo

**mlop** adopts a KISS philosophy that allows it to outperform all other tools in this category. Supporting high and stable data throughput should be *THE* top priority for efficient MLOps.
<video loop src='https://github.com/user-attachments/assets/efd9720e-6128-4278-85ec-ee6139a851af' alt="demo" width="1200" style="display: block; margin: auto;"></video>

<p align="center">
<strong>mlop</strong> logger (bottom left) v. a conventional logger (bottom right)
</p>

## 🚀 Getting Started

- Try **mlop** on our platform in [a notebook](https://colab.research.google.com/github/mlop-ai/mlop/blob/main/examples/intro.ipynb) & start integrating in just 5 lines of Python code:

```python
%pip install -Uq "mlop[full]"
import mlop

mlop.init(project="hello-world")
mlop.log({"e": 2.718})
mlop.finish()
```

- Self-host your very own **mlop** instance & get started in just 3 commands with **docker-compose**

```bash
git clone --recurse-submodules https://github.com/mlop-ai/server.git; cd server
cp .env.example .env
sudo docker-compose --env-file .env up --build
```

You may also learn more about **mlop** by checking out our [documentation](https://docs.mlop.ai/).

You can try everything out in our [introductory tutorial](https://colab.research.google.com/github/mlop-ai/mlop/blob/main/examples/intro.ipynb) and [torch tutorial](https://colab.research.google.com/github/mlop-ai/mlop/blob/main/examples/torch.ipynb).  

## 🫡 Vision

**mlop** is a platform built for and by ML engineers, supported by [our community](https://discord.gg/ybfVZgyFCX)! We were tired of the current state of the art in ML observability tools, and this tool was born to help mitigate the inefficiencies - specifically, we hope to better inform you about your model performance and training runs; and actually **save you**, instead of charging you, for your precious compute time! 

🌟 Be sure to star our repos if they help you ~
mia-amis 0.1.0.1
Assistant Module Interface System
Requires Python: >=3.8

# AMIS - Assistant Module Interface System

[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)]()
[![Code Style: Black](https://img.shields.io/badge/code%20style-black-000000.svg)]()

A modular system for creating AI assistants with hot configuration reloading and isolated components.

## 🌍 Supported Languages
[![EN](https://img.shields.io/badge/lang-English-blue)](README.md)
[![RU](https://img.shields.io/badge/lang-Русский-red)](README.ru.md)

## 🔍 Features

- 🧩 **Modular architecture**
- ⚡ **Hot reload**
- 🛡️ **Process isolation**
- 📁 **Autosave configurations**

## 🚀 Quick start

### Installation
```bash
pip install mia-amis
```

```bash
pip install -e .
```