semvec/pypi-packages huggingface dataset:
ark-sdk 0.1.53
Python SDK for ARK - Agentic Runtime for Kubernetes
Requires Python: >=3.9
Keywords: agentic-ai, agents, ai, ark, automation, kubernetes, llm, mckinsey

# ARK Python SDK

Python SDK for ARK - Agentic Runtime for Kubernetes. This SDK is part of the open source [Agents at Scale ARK](https://github.com/mckinsey/agents-at-scale-ark) project.

This package provides Python bindings for managing ARK agents, models, queries, and other resources in Kubernetes. It is partly generated from the ARK Custom Resource Definitions (CRDs), with additional capabilities layered on top.

## Installation

```bash
pip install ark-sdk
```

## Usage

### Kubernetes Resource Management

```python
from ark_sdk import ARKClientV1alpha1
from ark_sdk.models.agent_v1alpha1 import AgentV1alpha1, AgentV1alpha1Spec

# Initialize client
client = ARKClientV1alpha1(namespace="default")

# Create agent
agent = AgentV1alpha1(
    metadata={"name": "my-agent"},
    spec=AgentV1alpha1Spec(prompt="Hello", modelRef={"name": "gpt-4"})
)
created = client.agents.create(agent)

# Get, update, delete
agent = client.agents.get("my-agent")
agent.spec.prompt = "Updated"
client.agents.update(agent)
client.agents.delete("my-agent")
```

### Execution Engine Development

The SDK now includes utilities for building execution engines:

```python
from ark_sdk import BaseExecutor, ExecutorApp, ExecutionEngineRequest, Message

class MyExecutor(BaseExecutor):
    def __init__(self):
        super().__init__("MyEngine")
    
    async def execute_agent(self, request: ExecutionEngineRequest) -> List[Message]:
        # Your execution logic here
        return [Message(role="assistant", content="Hello from my engine!")]

# Create and run the executor
executor = MyExecutor()
app = ExecutorApp(executor, "MyEngine")
app.run(host="0.0.0.0", port=8000)
```

### Async Operations

```python
# List agents asynchronously
agents = await client.agents.a_list()

# Create query asynchronously
query = await client.queries.a_create(QueryV1alpha1(...))
```

## Execution Engine Types

The SDK provides common types for execution engines:

- `ExecutionEngineRequest` - Request format for agent execution
- `ExecutionEngineResponse` - Response format from execution engines
- `AgentConfig` - Agent configuration structure
- `Message` - Chat message format
- `BaseExecutor` - Abstract base class for execution engines
- `ExecutorApp` - FastAPI application setup for execution engines

## Documentation

For full documentation and examples, visit the [ARK project repository](https://github.com/mckinsey/agents-at-scale-ark).

## Requirements

- Python 3.9+
- Kubernetes cluster with ARK installed
- FastAPI and uvicorn (for execution engine development)
psiutils 0.2.28
Various TKinter utilities.
Requires Python: <3.13,>=3.10

# psiutils

Various utility classes and methods, mainly, but not exclusively Tkinter.

## Installation

```bash
pip install psiutils
```

## Main functionality

### Buttons

A classes and methods to organise the display and enabling of Tkinter buttons

### Constants

The module exposes certain constants particularly useful with Tkinter dialogs

e.g YES  (True), NO (False), CANCEL (None)

and Modes e.g. VIEW, NEW, EDIT, DELETE

### Drag manager

A drag and drop implementation for tkinter trees (see module documentation)

### Icecream

Set up icecream for an application.

### Known paths

Various path utilities including: return to path to a Windows known folder.

### Menus

A classes and methods to organise the display and enabling of Tkinter Menus


### Treeview

A classes and methods to organise the display  of Tkinter treeviews including sort and treeview with checkboxes.

### Utilities

Various utility classes and functions including:

* display_icon
* create_directories

    should really use (pathlib.Path('\<dir>').mkdir(parents=True, exist_ok=True))

### Widgets

Various widgets and utilities including:

* PsiText a Tkinter text widget that detects changes
* vertical_scroll_bar
* clickable_widget (change cursor when entered)
* status_bar
* WaitCursor
* separator_frame
* VerticalScrolledFrame
* Tooltip
* AboutFrame (provide labels and text in a dict)
impx 0.0.1
AI-powered engineering workflow framework — planning, validation, review, metrics.
Requires Python: >=3.12
Keywords: ai, engineering, metrics, review, validation, workflow

# imp

AI-powered engineering workflow framework — planning, validation, review, metrics.

Code execution is a pluggable slot for any agent runtime. Imp is the foreman, not the carpenter.

**Status:** Early development. Name claim release.
cobra-log 1.2.0
A lightweight and easy-to-use logging library for Python.
Requires Python: >=3.9

<div align="center">

<h2 id="title">🐱‍👓 cobra-log 🐱‍👓</h2>

[![PyPI version](https://img.shields.io/pypi/v/cobra-log.svg)](https://pypi.org/project/cobra-log/)
![Python](https://img.shields.io/pypi/pyversions/cobra-log?color=brightgreen)
[![codecov](https://codecov.io/gh/tinchen777/cobra-log/branch/main/graph/badge.svg)](https://codecov.io/gh/tinchen777/cobra-log)
![License](https://img.shields.io/github/license/tinchen777/cobra-log.svg)

[![Tests](https://github.com/tinchen777/cobra-log/actions/workflows/test.yml/badge.svg)](https://github.com/tinchen777/cobra-log/actions/workflows/test.yml)
![Github stars](https://img.shields.io/github/stars/tinchen777/cobra-log.svg)

</div>

## About

A lightweight and easy-to-use logging library for Python.

- Python: 3.9+

## Features

- 🚀 Vivid and detailed terminal output (need `cobra-color`).
- 🚀 Optional file persistence with rotation.
- 🚀 Simple, dependency-light design.

## Installation

Stable (once published):

```bash
pip install cobra-log
```

## Quick Start

- Simple warning

  ```python
  from cobra_log import warning, log_init

  log_init("your_log_file.log")

  warning("warning message")
  ```

## Example

```python
from cobra_log import (log_init, info, warning, error)

# Initialize the log system
log_init("log_save_path.log", use_color=True)

try:
    try:
        try:
            1 / 0
        except Exception as e:
            raise error("An error occurred during the test.", throw=None) from e
    except Exception as ee:
        raise error("An error(TimeoutError) occurred during the test.", throw=TimeoutError) from ee
except Exception:
    critical("A critical error occurred during the test.", throw=None)
```

## Requirements

- Python >= 3.9
- (Optional) cobra-color >= 1.3.0

## License

See LICENSE in the repository.

## Links

- Homepage/Repo: https://github.com/tinchen777/cobra-log.git
- Issues: https://github.com/tinchen777/cobra-log.git/issues
soniox-transcribe 0.1.0
Generate subtitles from audio/video using Soniox Speech-to-Text API
Requires Python: >=3.10

# soniox-transcribe

CLI tool for generating subtitles from audio/video files using the [Soniox](https://soniox.com) Speech-to-Text API.

## Features

- **Subtitle formats**: SRT, VTT, TXT
- **Video support**: Automatically extracts audio from video files via ffmpeg
- **Speaker diarization**: Speaker labels enabled by default
- **60+ languages**: With automatic language identification
- **Context support**: Custom terms and background text to improve accuracy (up to ~8,000 tokens)

## Installation

```bash
pip install soniox-transcribe
```

For video file support, [ffmpeg](https://ffmpeg.org) must be installed:

```bash
# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt install ffmpeg
```

## Configuration

Set your Soniox API key as an environment variable:

```bash
export SONIOX_API_KEY=<your-api-key>
```

Get your API key at [console.soniox.com](https://console.soniox.com).

Alternatively, create a `.env` file in your working directory:

```
SONIOX_API_KEY=<your-api-key>
```

## Usage

```bash
# Basic transcription (outputs .srt by default)
soniox-transcribe video.mp4

# Specify output file (format inferred from extension)
soniox-transcribe video.mp4 -o video.vtt

# Explicit format
soniox-transcribe audio.wav -f txt

# Transcribe from URL
soniox-transcribe --url https://example.com/audio.mp3

# Disable speaker diarization
soniox-transcribe interview.wav --no-speaker

# Add custom vocabulary
soniox-transcribe lecture.mp3 -t "Transformer" -t "BERT" -t "GPT"

# Provide background text for better accuracy
soniox-transcribe meeting.mp3 --context-text "Discussion about Q4 revenue targets"

# Use a context file
soniox-transcribe meeting.mp3 --context-text-file agenda.txt
```

### Options

| Option                | Description                                             |
| --------------------- | ------------------------------------------------------- |
| `-o`, `--output`      | Output file path (default: input with .srt extension)   |
| `-f`, `--format`      | Output format: `srt`, `vtt`, `txt`                      |
| `-u`, `--url`         | Transcribe from a public URL instead of a local file    |
| `-l`, `--language`    | Language hints (default: `zh`, `en`); repeatable         |
| `--no-speaker`        | Disable speaker diarization and labels                  |
| `--max-chars`         | Max characters per subtitle line (default: 42)          |
| `-m`, `--model`       | Soniox model (default: `stt-async-v4`)                  |
| `-t`, `--context-term`| Custom terms/vocabulary; repeatable                     |
| `--context-text`      | Background text for accuracy improvement                |
| `--context-text-file` | File containing background text                         |
| `-c`, `--context`     | JSON string with full context configuration             |
| `--context-file`      | JSON file with full context configuration               |

## Supported Formats

**Audio**: mp3, wav, flac, aac, ogg, m4a, wma

**Video**: mp4, mkv, avi, mov, webm, flv, wmv, m4v (requires ffmpeg)

## License

[MIT](LICENSE)
cortex-loom 0.3.0
Package for developing and publishing custom Kelvin Cortex Flows.
Requires Python: <3.13,>=3.11

# cortex-loom

Python utilities for working with the Kelvin Cortex platform.

This package helps you:

- Load and inspect Cortex-native data types locally
- Develop and test custom analysis logic before publishing
- Validate and publish reusable processing Flows to Cortex

## Features

- **Providers** for loading `DataObject`s from files  
  (`VideoObjectProvider`, `PoseObjectProvider`, etc.)
- **FlowBuilder** to build, validate signatures, and publish custom Flows
- Type hints and interfaces matching Cortex runtime expectations
- Helper functions for common data analysis patterns

## Current limitations (February 2026)

Custom Flows must return **only JSON-serialisable data**  

## Installation

```bash
pip install cortex-loom
```
myosdk 0.2.0
Python SDK for the MyoSapiens API
Requires Python: >=3.10

# MyoSapiens Python SDK

A modern Python SDK for the MyoSapiens API with type safety, async support, and an ergonomic API.

## Installation

```bash
pip install myosdk
```

## Quick Start

```python
from myosdk import Client

client = Client(api_key="ak_live_xxx")

# Upload, process, and download in one call
result = client.retarget(
    tracker="walk.c3d",
    markerset="markerset.xml",
    export_glb=True,
    stream_status=True,
    output_dir="out/",
)
```

**Requirements:** Python 3.10+ and a Myo API key from [dev.myolab.ai](https://dev.myolab.ai)

## Features

- **Type-safe** - Full Pydantic models with IDE autocomplete
- **Async support** - Use `AsyncClient` for async applications
- **Flexible inputs** - Accepts file paths, Asset objects, or asset IDs
- **Progress streaming** - Monitor job progress in real-time
- **Error handling** - Terra-style exceptions with detailed error information

## Documentation

Full SDK documentation, API reference, and examples are available at [docs.myolab.ai](https://docs.myolab.ai)

For tutorials and examples, see [github.com/myolab/myosdk_tutorials](https://github.com/myolab/myosdk_tutorials)
opclabs-pcap 5.83.1072
Ethernet and capture file communication package.
Keywords: QuickOPC, OPC, UA, PubSub, Ethernet, UDP, capture

OpcLabs.Pcap (NuGet) / opclabs_pcap (Python)
===========================================

The OpcLabs.Pcap (NuGet) or opclabs_pcap (Python) package provides Ethernet 
(and UDP) communication features, using the PacketDotNet and SharpPcap 
libraries. Its current use is with the OpcLabs.QuickOpc / opclabs_quickopc 
package for OPC UA PubSub transport protocol mappings based on Ethernet, or 
for work with Ethernet or UDP capture files.
opclabs-mqttnet 5.83.1072
MQTT communication package based on MQTTnet and MQTTnet.Extensions.ManagedClient libraries by Christian Kratky.
Keywords: QuickOPC, OPC, UA, PubSub, MQTT

OpcLabs.MqttNet (NuGet) / opclabs_mqttnet (Python)
==================================================

The OpcLabs.MqttNet (NuGet) or opclabs_mqttnet (Python) package provides MQTT 
communication features for the OPC Labs products, building on MQTTnet and 
MQTTnet.Extensions.ManagedClient libraries by Christian Kratky.Its current 
use is with the OpcLabs.QuickOpc / opclabs_quickopc package for OPC UA PubSub 
transport protocol mappings based on MQTT.

In most cases, this package is recommended over the OpcLabs.Mqtt / 
opclabs_mqtt package which is based on a different library and has less
features.

Documentation: [OpcLabs.MqttNet communication package](https://kb.opclabs.com/OpcLabs.MqttNet_communication_package)
opclabs-mqtt 5.83.1072
MQTT communication package based on System.Net.Mqtt library by Xamarin (Microsoft).
Keywords: QuickOPC, OPC, UA, PubSub, MQTT

OpcLabs.Mqtt (NuGet) / opclabs_mqtt (Python)
============================================

The OpcLabs.Mqtt (NuGet) or opclabs_mqtt (Python) package provides MQTT 
communication features for the OPC Labs products, using the System.Net.Mqtt 
library. Its current use is with the OpcLabs.QuickOpc / opclabs_quickopc 
package for OPC UA PubSub transport protocol mappings based on MQTT.

In most cases, it is recommended that you rather use the OpcLabs.MqttNet / 
opclabs_mqttnet package which is based on a different library and has more 
features.

Documentation: [OpcLabs.Mqtt communication package](https://kb.opclabs.com/OpcLabs.Mqtt_communication_package)