semvec/pypi-packages huggingface dataset:
agent-dist 0.2.0
A lightweight agentic mesh for orchestrating AI agents.
Requires Python: >=3.11

<<<<<<< HEAD
# agent_dist
Distributed agent orchestration framework with registry, hierarchical routing, ReAct execution, and traceable multi-agent workflows.
=======
# AgentFlows

A lightweight agentic mesh for orchestrating AI agents in a hospital environment.

## ⚙️ Configuration

Copy the example configuration file:
```bash
cp .env.example .env
```
Edit `.env` to set your LLM provider.

### Supported LLM Providers
- **Ollama** (Default): Run local models via `ollama serve`.
- **Groq**: Fast inference. Set `LLM_PROVIDER=groq` and `GROQ_API_KEY`.
- **OpenAI**: Set `LLM_PROVIDER=openai` and `LLM_API_KEY`.

## Installation
```bash
pip install agentflows
```

## Running

**Registry:**
```bash
python -m agentflows.registry.app
```

**Orchestrator:**
```bash
python -m agentflows.orchestrator.app
```
>>>>>>> f31a0e6 (Initial commit)
snakemake-software-deployment-plugin-conda 0.3.5
Software deployment plugin for Snakemake using rattler to deploy conda packages.
Requires Python: <4.0,>=3.11

# snakemake-software-deployment-plugin-conda

A Snakemake software deployment plugin for conda packages, using [py-rattler](https://conda.github.io/rattler/py-rattler) for ultra fast and robust environment deployment.
snakemake-interface-software-deployment-plugins 0.10.2
This package provides a stable interface for interactions between Snakemake and its software deployment plugins.
Requires Python: <4.0,>=3.11

# Stable interfaces and functionality for Snakemake software deployment plugins

This package provides a stable interface for interactions between Snakemake and its software deployment plugins.
It is still a work in progress, but completing it is the next big thing on our list.
mnase-classifier 0.2.5
Add your description here
Requires Python: >=3.13

<!--
SPDX-FileCopyrightText: 2026 Laurent Modolo <laurent.modolo@cnrs.fr>, Lauryn Trouillot <lauryn.trouillot@ens-lyon.fr>

SPDX-License-Identifier: AGPL-3.0-or-later
-->

[![pipeline status](http://gitbio.ens-lyon.fr/LBMC/Bernard/mnase_classifier/badges/main/pipeline.svg)](http://gitbio.ens-lyon.fr/LBMC/Bernard/mnase_classifier/-/commits/main)
[![coverage report](http://gitbio.ens-lyon.fr/LBMC/Bernard/mnase_classifier/badges/main/coverage.svg)](http://gitbio.ens-lyon.fr/LBMC/Bernard/mnase_classifier/-/commits/main)



# mnase_classifier

A detailed description of this project can be found [here](https://lbmc.gitbiopages.ens-lyon.fr/biocomp/projects/2025/2025_12_19_bernard/)


## Getting started

Getting the project up and running is as simple as:

### Install from pypi

```bash
pipx install mnase-classifier
```

### Install from source

if you want to install it from source you will need [uv](https://github.com/astral-sh/uv) installed

clone the repository

```bash
git clone git@gitbio.ens-lyon.fr:LBMC/Bernard/mnase_classifier.git
cd mnase_classifier
```

Initialize the **uv** environment:

```bash
uv sync
```

Print help:

```bash
uv run mnase_classifier --help
```

### Usage example

Classify paired reads from a BAM file into groups based on insert size:

```bash
uv run mnase_classifier --bam_input path/to/file.bam --outdir outdir --means "110 150 250 300" -v
```

The `-v` flag enables verbose output for debugging.