semvec/pypi-packages huggingface dataset:
dryft 0.2.2
Dryft CLI and daemon — sync agent context with Dryft server
Requires Python: >=3.10
Keywords: dryft, ai, agents, coordination, git

# Dryft Python client

Same CLI as the Node client, installable via **pip** per the PRD:

```bash
pip install dryft
dryft init
dryft start
```

From source (repo root):

```bash
pip install -e ./client-py
```

Commands: `init`, `start`, `stop`, `status`, `config`, `push-notify`. Implementation follows the same phases as the Node client in `client/`.
yscope-clp-core 0.9.1b1
YScope CLP (Compressed Log Processor) Core Python Distribution
Requires Python: >=3.10

# YScope CLP (Compressed Log Processor) Core Python Distribution

This project packages [YScope CLP core][clp-core-docs] as a Python distribution, providing the CLP
core command line tools and a Python binding for performing CLP archive operations programmatically.

## Installation

To install the package:

```shell
pip install yscope-clp-core
```

See the PyPI project page for [release][pypi-release] details.

## Scope and compatibility

The current package includes:
* The `clp-s` binary
* Python APIs `open_archive` and `search_archive` for working with CLP archives.

## Documentation

For detailed usage of CLP core features and design documentation, see the official CLP documents:

* [**clp-s**][clp-s-docs]: CLP for JSON logs.

[clp-core-docs]: https://docs.yscope.com/clp/main/user-docs/index.html#core
[clp-s-docs]: https://docs.yscope.com/clp/main/user-docs/core-clp-s
[pypi-release]: https://pypi.org/project/yscope-clp-core
ctrlsys 1.0.19
Python bindings for SLICOT C library (control theory)
Requires Python: >=3.11

# ctrlsys

[![PyPI version](https://img.shields.io/pypi/v/ctrlsys)](https://pypi.org/project/ctrlsys/)
[![Build Status](https://github.com/jamestjsp/ctrlsys/actions/workflows/test.yml/badge.svg)](https://github.com/jamestjsp/ctrlsys/actions)
[![Documentation](https://readthedocs.org/projects/ctrlsys/badge/?version=latest)](https://ctrlsys.readthedocs.io/en/latest/)
[![License: BSD-3-Clause](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](LICENSE)

Python bindings for **SLICOT** (Subroutine Library In COntrol Theory) - numerical routines for control systems analysis and design. This is a low-level API primarily targeting AI agents and higher-level libraries.

> **Note:** This package was previously published as `slicot` on PyPI. Install with `pip install ctrlsys`.

## Installation

```bash
pip install ctrlsys
```

## Features

- **600+ routines** for control systems
- **State-space methods**: Riccati, Lyapunov, pole placement
- **Model reduction**: Balance & Truncate, Hankel-norm
- **System identification**: MOESP, N4SID
- **NumPy integration**: Column-major arrays

## Usage

While you can use this library directly, it's recommended to access it through AI coding agents that understand control theory conventions and SLICOT's API patterns.

For agent skills (Claude Code, Cursor, etc.), see [control-skills](https://github.com/jamestjsp/control-skills).

## Quick Start

```python
import numpy as np
import ctrlsys

# Controllability analysis
A = np.array([[1, 2], [3, 4]], order='F')
B = np.array([[1], [0]], order='F')

a_out, b_out, ncont, z, tau, info = ctrlsys.ab01md('I', A, B.flatten(), 0.0)
print(f"Controllable dimension: {ncont}")
```

## Column-Major Arrays

SLICOT uses Fortran conventions:

```python
A = np.array([[1, 2], [3, 4]], order='F')  # Required!
```

## Contributions

I don't accept direct contributions. Issues and PRs are welcome for illustration, but won't be merged directly. An AI agent reviews submissions and independently decides whether/how to address them. Bug reports appreciated.

## License

This project (C11 translation and Python bindings) is BSD-3-Clause. See [LICENSE](LICENSE).

The original SLICOT library is copyright © 1996-2025 The SLICOT Team, maintained by [NICONET e.V.](https://www.slicot.org/), and licensed under BSD-3-Clause. See [LICENSE-SLICOT](LICENSE-SLICOT).
sik-llms 0.3.29
Lightweight, easy, and consistent LLM-interface across providers and functionality.
Requires Python: >=3.11

# sik-llms

- Easy llm interface; eliminates parsing json responses or building up json for functions/tools
    - OpenAI and any OpenAI-compatible API
    - Anthropic
- Sync and Async support
- Functions/Tools
- Structured Output
    - Supports Anthropic models even though structured output is not natively supported in their models
- Reasoning mode in OpenAI and Anthropic
- ReasoningAgent that iteratively reasons and calls tool

See [examples](https://github.com/shane-kercheval/sik-llms/blob/main/examples/examples.ipynb)

```
from sik_llms import create_client, user_message, ResponseChunk

model = create_client(
    model_name='gpt-4o-mini',  # or e.g. 'claude-3-7-sonnet'
    temperature=0.1,
)
messages = [
    system_message("You are a helpful assistant."),
    user_message("What is the capital of France?"),
]

# sync
response = model(messages=messages)

# async
response = await model.run_async(messages=messages)

# async streaming
responses = []
summary = None
async for response in model.stream(messages=messages):
    if isinstance(response, TextChunkEvent):
        print(response.content, end="")
        responses.append(response)
    else:
        summary = response

print(summary)
```

## Installation

`uv install sik-llms` or `pip install sik-llms`