semvec/pypi-packages huggingface dataset:
kryten-shell 0.1.4
Interactive TUI shell for inspecting and controlling the Kryten ecosystem
License: MIT
Requires Python: <4.0,>=3.10
Keywords: kryten, cytube, tui, shell, textual

# Kryten Shell

Interactive TUI shell for inspecting and controlling the Kryten ecosystem.

## Features

- **Live Monitoring**: Watch events, chat, and playlist changes in real-time
- **Interactive Commands**: Send messages, manage playlist, query state with natural syntax
- **KV Store Inspector**: Browse and inspect NATS KV store contents
- **Event Streaming**: View all Kryten ecosystem events with filtering
- **Solarized Dark Theme**: Easy on the eyes for extended sessions
- **Command History**: Persistent history across sessions with up/down navigation
- **Tab Completion**: Autocomplete commands as you type

## Installation

```bash
cd kryten-shell
poetry install
```

## Usage

```bash
# Start with defaults (localhost:4222)
poetry run kryten-shell

# Or use the short alias
poetry run ksh

# Connect to specific NATS server
poetry run kryten-shell --host 192.168.1.10 --port 4222

# Join a channel on startup
poetry run kryten-shell --channel cyberia
```

## Keyboard Shortcuts

| Key | Action |
|-----|--------|
| `F1` | Show help |
| `F2` | Switch to Chat tab |
| `F3` | Switch to Playlist tab |
| `F4` | Switch to Events tab |
| `F5` | Switch to KV Store tab |
| `Ctrl+R` | Refresh current view |
| `Ctrl+L` | Clear current log |
| `Ctrl+Q` | Quit |
| `Escape` | Focus command input |
| `↑/↓` | Navigate command history |
| `Tab` | Autocomplete command |

## Commands

### Connection
- `connect` - Connect to NATS server
- `disconnect` - Disconnect from server
- `channel [name]` - Join channel or show current
- `discover` - Discover available channels

### Chat
- `msg <message>` - Send a chat message

### Playlist
- `playlist` - Show playlist
- `nowplaying` - Show current video
- `skip` - Skip current video

### KV Store
- `kv buckets` - List KV buckets
- `kv get <bucket> <key>` - Get a value
- `kv keys <bucket>` - List keys in bucket

### Utility
- `help [command]` - Show help
- `clear` - Clear current view
- `refresh` - Refresh current view
- `filter [pattern]` - Filter events by type
- `status` - Show connection status
- `quit` - Exit shell

## Configuration

Configuration is stored in the platform-specific config directory:
- Windows: `%APPDATA%\kryten\kryten-shell\config.json`
- Linux: `~/.config/kryten-shell/config.json`
- macOS: `~/Library/Application Support/kryten-shell/config.json`

Example config:
```json
{
  "nats": {
    "host": "localhost",
    "port": 4222
  },
  "channel": "cyberia",
  "show_timestamps": true,
  "max_chat_lines": 500,
  "max_event_lines": 1000
}
```

## Development

```bash
# Install with dev dependencies
poetry install --with dev

# Run with textual dev tools
poetry run textual run kryten_shell.app:KrytenShellApp

# Run tests
poetry run pytest

# Type checking
poetry run mypy kryten_shell

# Linting
poetry run ruff check kryten_shell
```

## License

MIT
mypy-gh-action-report 0.2.4
Notify Mypy output via GitHub Workflow Commands
License: MIT
Requires Python: <4.0,>=3.10

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v1.json)](https://github.com/astral-sh/ruff)
[![ty](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ty/main/assets/badge/v0.json)](https://github.com/astral-sh/ruff)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/mypy-gh-action-report.svg)](https://pypi.python.org/pypi/mypy-gh-action-report/)
[![PyPI](https://img.shields.io/pypi/v/mypy-gh-action-report.svg)](https://pypi.python.org/pypi/mypy-gh-action-report/)
[![PyPI downloads](https://img.shields.io/pypi/dm/mypy-gh-action-report?style=flat-square)](https://pypi.python.org/pypi/mypy-gh-action-report/)
[![Tests workflow](https://github.com/bc291/mypy-gh-action-report/actions/workflows/tests.yml/badge.svg)](https://github.com/bc291/mypy-gh-action-report)

# mypy-gh-action-report

Notify Mypy output via [GitHub Workflow Commands](https://docs.github.com/en/actions/using-workflows/workflow-commands-for-github-actions)

## Installation

```bash
pip install mypy-gh-action-report
```

## Docs

Available [here](https://bc291.github.io/mypy-gh-action-report/)

## Thanks

- [github-action-utils](https://github.com/saadmk11/github-action-utils)
- [typer](https://github.com/tiangolo/typer)
kryten-webui 0.1.1
Kryten web interface service - provides web dashboard and control panel
License: MIT
Requires Python: <4.0,>=3.10
Keywords: cytube, moderation, chat, nats, microservices

"# Kryten WebUI

Kryten web interface service - provides web dashboard and control panel for CyTube.

## Features

- Real-time chat message monitoring
- User tracking and management
- Event-driven architecture using NATS
- Extensible moderation rules

## Installation

### Prerequisites

- Python 3.10 or higher
- Poetry
- NATS server running
- kryten-py library

### Setup

1. Install dependencies:
```bash
poetry install
```

2. Copy the example configuration:
```bash
cp config.example.json config.json
```

3. Edit `config.json` with your settings:
```json
{
  "nats_url": "nats://localhost:4222",
  "nats_subject_prefix": "cytube",
  "service_name": "kryten-webui"
}
```

## Usage

### Running the Service

Using Poetry:
```bash
poetry run kryten-webui --config config.json
```

Using the startup script (PowerShell):
```powershell
.\start-webui.ps1
```

Using the startup script (Bash):
```bash
./start-webui.sh
```

### Command Line Options

- `--config PATH`: Path to configuration file (default: `/etc/kryten/webui/config.json`)
- `--log-level LEVEL`: Set logging level (DEBUG, INFO, WARNING, ERROR)

## Event Handling

The service currently listens for:

- **chatMsg**: Chat messages for dashboard display
- **usercount**: User count updates
- **playlist**: Playlist updates for dashboard

## Development

### Running Tests

```bash
poetry run pytest
```

### Linting

```bash
poetry run ruff check .
```

### Formatting

```bash
poetry run black .
```

## License

MIT License - see LICENSE file for details
"
kryten-bingo 0.1.1
Kryten bingo game service - manages bingo games and card tracking
License: MIT
Requires Python: <4.0,>=3.10
Keywords: cytube, moderation, chat, nats, microservices

"# Kryten Bingo

Kryten bingo game service - manages bingo games and card tracking for CyTube.

## Features

- Real-time chat message monitoring
- User tracking and management
- Event-driven architecture using NATS
- Extensible moderation rules

## Installation

### Prerequisites

- Python 3.10 or higher
- Poetry
- NATS server running
- kryten-py library

### Setup

1. Install dependencies:
```bash
poetry install
```

2. Copy the example configuration:
```bash
cp config.example.json config.json
```

3. Edit `config.json` with your settings:
```json
{
  "nats_url": "nats://localhost:4222",
  "nats_subject_prefix": "cytube",
  "service_name": "kryten-bingo"
}
```

## Usage

### Running the Service

Using Poetry:
```bash
poetry run kryten-bingo --config config.json
```

Using the startup script (PowerShell):
```powershell
.\start-bingo.ps1
```

Using the startup script (Bash):
```bash
./start-bingo.sh
```

### Command Line Options

- `--config PATH`: Path to configuration file (default: `/etc/kryten/bingo/config.json`)
- `--log-level LEVEL`: Set logging level (DEBUG, INFO, WARNING, ERROR)

## Event Handling

The service currently listens for:

- **chatMsg**: Chat messages for bingo commands

## Development

### Running Tests

```bash
poetry run pytest
```

### Linting

```bash
poetry run ruff check .
```

### Formatting

```bash
poetry run black .
```

## License

MIT License - see LICENSE file for details
"
neuro-int-mamba 0.1.0
A Bio-inspired Mamba Architecture for Dexterous Manipulation with Intrinsic Neural Timescales
License: MIT
Requires Python: >=3.13

# Neuro-INT Mamba

A Bio-inspired Mamba Architecture for Dexterous Manipulation with Intrinsic Neural Timescales (INT).

## Features

- **Dual-Stream INT**: Parallel fast (sensory) and slow (cognitive) streams.
- **Predictive Coding**: Efference copy loop for error-driven learning.
- **Chandelier Gating**: Inhibitory control inspired by Chandelier cells.
- **Spinal Reflex**: Low-level feedback for immediate response.
- **Real-time I/O**: $O(1)$ inference step for closed-loop control.

## Installation

```bash
pip install neuro-int-mamba
```

## Usage

```python
import torch
from neuro_int_mamba import NeuroINTMamba

input_dims = {
    'proprio': 54,
    'tactile': 100,
    'visual': 256,
    'goal': 32
}

model = NeuroINTMamba(input_dims, model_dim=512, num_layers=6)

# Real-time control loop
states = None
predictions = None

while True:
    # Get sensory data
    p, t, v, g = get_sensors() 
    
    # Step model
    motor_cmd, states, predictions = model.step(p, t, v, g, states, predictions)
    
    # Apply motor command
    apply_motor(motor_cmd)
```

## Development

This project uses [Astral](https://astral.sh/) tools for high-performance development:
- **uv**: Package management
- **ruff**: Linting and formatting
- **ty**: Type checking

```bash
uv run ruff check .
uv run ty check .
uv run pytest
```

## Publishing to PyPI

This project is configured to be built with `hatchling` and published via `uv`.

1. **Build the package**:
   ```bash
   uv build
   ```

2. **Publish to PyPI**:
   ```bash
   # Use __token__ as username and your PyPI API token as password
   uv publish
   ```

## License

MIT
timestep 2026.0.57
Timestep AI - Agent execution framework with cross-language state persistence and multi-model support for OpenAI Agents
License: MIT
Requires Python: >=3.11

# Timestep (Python)

Python bindings for the Timestep Agents SDK. See the root `README.md` for the full story; this file highlights Python-specific setup.

## Install
```bash
pip install timestep
```

## Prerequisites (Python)
- `OPENAI_API_KEY`
- **PostgreSQL**: Set `PG_CONNECTION_URI=postgresql://user:pass@host/db`

## Quick start
```python
from timestep import run_agent, RunStateStore
from agents import Agent, Session

agent = Agent(model="gpt-4.1")
session = Session()
state_store = RunStateStore(agent=agent, session_id=await session._get_session_id())

result = await run_agent(agent, input_items, session, stream=False)

if result.interruptions:
    await state_store.save(result.to_state())
```

## Cross-language resume
Save in Python, load in TypeScript with the same `session_id` and `RunStateStore.load()`.

## Model routing
Use `MultiModelProvider` if you need OpenAI + Ollama routing:
```python
from timestep import MultiModelProvider, MultiModelProviderMap, OllamaModelProvider

provider_map = MultiModelProviderMap()
provider_map.add_provider("ollama", OllamaModelProvider())
model_provider = MultiModelProvider(provider_map=provider_map)
```