semvec/pypi-packages huggingface dataset:
stackloop 2.0.0
AI-powered debugging agent that automatically runs, analyzes, and fixes your code
Requires Python: <4.0,>=3.10
Keywords: ai, debugging, automation, code-fixing, developer-tools

# StackLoop

AI-powered debugging agent that automatically runs, analyzes, and fixes your code.

[![YouTube](https://img.shields.io/badge/YouTube-Tutorial-red)](https://youtu.be/wKlz5Zx3AdI)
[![PyPI - Version](https://img.shields.io/pypi/v/stackloop.svg)](https://pypi.org/project/stackloop)


## Installation
```bash
pip install stackloop
```

## Usage
```bash
# Run in current directory
stackloop run

# Run in specific directory
stackloop run /path/to/your/project
```

## Features

- 🤖 AI-powered error analysis
- 🔧 Automatic code fixes
- 🔄 Iterative debugging
- 💾 Safe backups before modifications
- 🎨 Beautiful CLI interface

## Requirements

- Python 3.9+
- API keys for inference API providers (Groq, OpenAI, Anthropic, etc.)
## Configuration

Create a `.env` file in your project directory with at least one API key:
```
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
GROQ_API_KEY=your_key_here
```

## License

MIT License
elias-mcp-server-elpadev 0.0.7
A small MCP server
Requires Python: >=3.12

# Example Package

This is a simple example package. You can use
[GitHub-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content

mcp-name: io.github.elpadev/sample-mcp-server-python-package
exo-oscilloscope 0.0.3
A Python-based exosuit oscilloscope
Requires Python: <3.14,>=3.11

# Exosuit Oscilloscope
[![Coverage Status](https://coveralls.io/repos/github/TUM-Aries-Lab/exo-oscilloscope/badge.svg?branch=main)](https://coveralls.io/github/TUM-Aries-Lab/exo-oscilloscope?branch=main)
![Docker Image CI](https://github.com/TUM-Aries-Lab/exo-oscilloscope/actions/workflows/ci.yml/badge.svg)

A data visualizer to help view the exosuit data streams.

## Install
To install the library run:

```bash
pip install exo-oscilloscope
```
OR
```bash
pip install git+https://github.com/TUM-Aries-Lab/exo-oscilloscope.git@<specific-tag>
```

## Development
0. Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer)
1. Install [pyenv](https://github.com/pyenv/pyenv?tab=readme-ov-file#installation)
2. ```pyenv install <desired-python-version>  # install the required python version```
3. ```pyenv global <desired-python-version>  # set the required python version```
4. ```git clone git@github.com:TUM-Aries-Lab/exo-oscilloscope.git```
5. `make init` to create the virtual environment and install dependencies
6. `make format` to format the code and check for errors
7. `make test` to run the test suite
8. `make clean` to delete the temporary files and directories

## Publishing
It's super easy to publish your own packages on PyPI. To build and publish this package run:

```bash
poetry build
poetry publish  # make sure your version in pyproject.toml is updated
```
The package can then be found at: https://pypi.org/project/exo-oscilloscope

## Module Usage
```python
"""Basic docstring for my module."""

from exo_oscilloscope.plotter import ExoPlotter

def main() -> None:
    """Run a simple demonstration."""
    gui = ExoPlotter()
    gui.run()

if __name__ == "__main__":
    main()
```

## Program Usage
```bash
poetry run python -m exo_oscilloscope
```
agentUniverse 0.0.19
agentUniverse is a framework for developing applications powered by multi-agent base on large language model.
Requires Python: <4.0,>=3.10

# agentUniverse

## Overview
agentUniverse is a framework for developing applications powered by multi-agent base on large language model.  It provides all the essential components for building a single agent, and a multi-agent collaboration mechanism which  serves as a pattern factory that allowing developers to build and customize multi-agent collaboration patterns. With this framework,  developers can easily construct multi-agent applications, and share the pattern practices from different technical  and business fields.

The framework will come with several pre-install multi-agent collaboration patterns which have been proven effective in real business scenarios, and will continue to be enriched in the future. Patterns that are currently about to be released include:

- PEER pattern：
This pattern utilizes four distinct agent roles: Plan, Execute, Express, and Review, to achieve a multi-step breakdown and sequential execution of a complex task. It also performs autonomous iteration based on evaluative feedback which enhancing performance in reasoning and analytical tasks. 


- DOE pattern：
This pattern consists of three agents: Data-fining agent, which is designed to solve data-intensive and high-computational-precision task; Opinion-inject agent, which combines the data results from first agent and the expert opinions which are pre-collected and structured; the third agent, Express agent generates the final result base on given document type and language style.

More patterns are coming soon...