semvec/pypi-packages huggingface dataset:
communications 0.1.0
Add your description here
Requires Python: >=3.11

# communications

Initial PyPI package scaffold for `communications`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
teams 0.1.0
Add your description here
Requires Python: >=3.11

# teams

Initial PyPI package scaffold for `teams`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
arrangements 0.1.0
Add your description here
Requires Python: >=3.11

# arrangements

Initial PyPI package scaffold for `arrangements`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
rektrag 0.0.2
No-brainer vectorless RAG combining docling and toon-python
Requires Python: >=3.12

<div style="text-align: center;">
  <img src="https://capsule-render.vercel.app/api?type=transparent&fontColor=0047AB&text=RektRAG&height=120&fontSize=90">
</div>
<p align="center">
  <a href="https://github.com/RektPunk/RektRAG/releases/latest">
    <img alt="release" src="https://img.shields.io/github/v/release/RektPunk/RektRAG.svg">
  </a>
  <a href="https://pypi.org/project/RektRAG">
    <img alt="Pythonv" src="https://img.shields.io/pypi/pyversions/RektRAG.svg?logo=python&logoColor=white">
  </a>
  <a href="https://github.com/RektPunk/RektRAG/blob/main/LICENSE">
    <img alt="License" src="https://img.shields.io/github/license/RektPunk/RektRAG.svg">
  </a>
</p>


RektRAG introduces a lightweight, tree-based RAG designed for high-precision retrieval with minimal token overhead. RektRAG utilizes [Docling](https://github.com/docling-project/docling) for structured parsing and introduces the [TOON](https://github.com/toon-format/toon) format to optimize context window usage.

By decoupling the core logic from specific LLM providers, RektRAG allows integration with any model. It leverages asynchronous processing and hierarchical summarization to provide a "No-brainer" experience for complex document retrieval.

# Installation
Install using pip:
```bash
pip install rektrag
```

# Usage
- **RektEngine**: Orchestrates document ingestion, state management, and multi-document retrieval.
- **LLMProvider**: Easily plug in OpenAI, Anthropic, or local LLMs by implementing the interface.

# Example
Please refer to the [**Examples**](https://github.com/RektPunk/RektRAG/tree/main/examples) provided for further clarification.
agendas 0.1.0
Add your description here
Requires Python: >=3.11

# agendas

Initial PyPI package scaffold for `agendas`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
proposals 0.1.0
Add your description here
Requires Python: >=3.11

# proposals

Initial PyPI package scaffold for `proposals`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
atomsql 0.0.3
A lightweight Object-Relational Mapping (ORM) library for Python with support for SQLite and PostgreSQL
Requires Python: >=3.12
Keywords: orm, database, sqlite, postgresql, sql

# AtomSQL

AtomSQL is a small, lightweight Object-Relational Mapping (ORM) library for Python.

## Features

- Simple and intuitive API
- Lightweight with minimal dependencies
- Basic CRUD operations support

## Installation

```bash
pip install atomsql
```

## Usage

```python
from atomsql import Database

# Usage examples coming soon...
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License

[MIT](https://choosealicense.com/licenses/mit/)
harbor 0.1.44
A framework for evaluating and optimizing agents and models using sandboxed environments.
Requires Python: >=3.12

# Harbor

 [![](https://dcbadge.limes.pink/api/server/https://discord.gg/6xWPKhGDbA)](https://discord.gg/6xWPKhGDbA)
[![Docs](https://img.shields.io/badge/Docs-000000?style=for-the-badge&logo=mdbook&color=105864)](https://harborframework.com/docs)



Harbor is a framework from the creators of [Terminal-Bench](https://www.tbench.ai) for evaluating and optimizing agents and language models. You can use Harbor to:

- Evaluate arbitrary agents like Claude Code, OpenHands, Codex CLI, and more.
- Build and share your own benchmarks and environments.
- Conduct experiments in thousands of environments in parallel through providers like Daytona and Modal. 
- Generate rollouts for RL optimization.


## Installation

```bash tab="uv"
uv tool install harbor
```
or
```bash tab="pip"
pip install harbor
```


## Example: Running Terminal-Bench-2.0
Harbor is the offical harness for [Terminal-Bench-2.0](https://github.com/laude-institute/terminal-bench-2):

```bash 
export ANTHROPIC_API_KEY=<YOUR-KEY> 
harbor run --dataset terminal-bench@2.0 \
   --agent claude-code \
   --model anthropic/claude-opus-4-1 \
   --n-concurrent 4 
```

This will launch the benchmark locally using Docker. To run it on a cloud provider (like Daytona) pass the `--env` flag as below:

```bash 

export ANTHROPIC_API_KEY=<YOUR-KEY> 
export DAYTONA_API_KEY=<YOUR-KEY>
harbor run --dataset terminal-bench@2.0 \
   --agent claude-code \
   --model anthropic/claude-opus-4-1 \
   --n-concurrent 100 \
   --env daytona
```

To see all supported agents, and other options run:

```bash
harbor run --help
```

To explore all supported third pary benchmarks (like SWE-Bench and Aider Polyglot) run:

```bash
harbor datasets list
```

To evaluate an agent and model one of these datasets, you can use the following command:

```bash
harbor run -d "<dataset@version>" -m "<model>" -a "<agent>"
```

## Citation

If you use **Harbor** in academic work, please cite the software:

```
@software{harborframeworkteam2026harborframework,
      title={Harbor: A framework for evaluating and optimizing agents and models in container environments.}, 
      author={Harbor Framework Team},
      year={2026},
      url={https://github.com/laude-institute/harbor},
}
```
accelbyte-py-sdk-feat-auth 0.4.0
AccelByte Python SDK - Feature - Auth
Requires Python: >=3.9

# AccelByte Modular Python SDK - Feature Module

This is a feature module for the [AccelByte Modular Python SDK](https://github.com/AccelByte/accelbyte-python-modular-sdk) package.

## Setup

This SDK requires Python 3.9 to be installed.

### Install with Pip

Install the core package from PyPI

```sh
pip install accelbyte-py-sdk-core
```

install *this* service

```sh
pip install accelbyte-py-sdk-feat-auth
```

and then install any service or feature you want

```sh
pip install accelbyte-py-sdk-service-{ SERVICE }
```

```sh
pip install accelbyte-py-sdk-feat-{ FEATURE }
```

or install everything

```sh
pip install accelbyte-py-sdk-all
```

## Usage

See [README.md](https://github.com/AccelByte/accelbyte-python-modular-sdk/blob/main/README.md)
plaso 20260119
Plaso (log2timeline) - Super timeline all the things
Requires Python: >=3.10

## Plaso (Plaso Langar Að Safna Öllu) - super timeline all the things

Plaso (Plaso Langar Að Safna Öllu), or *super timeline all the things*, is a
Python-based engine used by several tools for automatic creation of timelines.
Plaso default behavior is to create super timelines but it also supports
creating more [targeted timelines](http://blog.kiddaland.net/2013/02/targeted-timelines-part-i.html).

These timelines support digital forensic investigators/analysts, to correlate
the large amount of information found in logs and other files found on an
average computer.

### A longer version

The initial purpose of Plaso was to collect all timestamped events of interest 
on a computer system and have them aggregated in a single place for computer 
forensic analysis (aka Super Timeline).

However Plaso has become a framework that supports:

* adding new parsers or parsing plug-ins;
* adding new analysis plug-ins;
* writing one-off scripts to automate repetitive tasks in computer forensic analysis or equivalent.

And is moving to support:

* adding new general purpose parses/plugins that may not have timestamps associated to them;
* adding more analysis context;
* tagging events;
* allowing more targeted approach to the collection/parsing.

### Also see

* [Homepage](https://github.com/log2timeline/plaso)
* [Downloads](https://github.com/log2timeline/plaso/releases)
* [Documentation](https://plaso.readthedocs.io)
* Contact information:
  * Plaso channel on [Open Source DFIR Slack community](https://github.com/open-source-dfir/slack)
  * Mailing list for general discussions: [log2timeline-discuss](https://groups.google.com/forum/#%21forum/log2timeline-discuss)
  * Mailing list for development: [log2timeline-dev](https://groups.google.com/forum/#%21forum/log2timeline-dev)