semvec/pypi-packages huggingface dataset:
usehid 0.1.0
Cross-platform virtual HID device library for AI agents
License: MIT
Requires Python: >=3.8
Keywords: hid, virtual, mouse, keyboard, automation, ai, agent

# useHID Python Bindings

Cross-platform virtual HID device library for Python.

## Installation

```bash
pip install usehid
```

## Quick Start

```python
from usehid import Mouse, Keyboard, AgentHID

# Basic Mouse
mouse = Mouse()
mouse.move_by(100, 50)
mouse.click()
mouse.double_click("left")
mouse.scroll(-3)  # Scroll down

# Basic Keyboard
keyboard = Keyboard()
keyboard.type_text("Hello, World!")
keyboard.press("enter")
keyboard.combo(["ctrl"], "c")  # Copy

# For AI Agents
agent = AgentHID()

# Execute actions from dict
result = agent.execute({
    "action": "mouse_move",
    "x": 100,
    "y": 200
})

result = agent.execute({
    "action": "type",
    "text": "Hello from AI!"
})

result = agent.execute({
    "action": "key_combo",
    "modifiers": ["ctrl", "shift"],
    "key": "s"
})
```

## Available Actions

### Mouse Actions

- `mouse_move` - Move mouse by offset (`x`, `y`)
- `mouse_click` - Click button (`button`: "left"/"right"/"middle")
- `mouse_double_click` - Double click
- `mouse_down` - Press button
- `mouse_up` - Release button
- `mouse_scroll` - Scroll wheel (`delta`: positive=up, negative=down)

### Keyboard Actions

- `type` - Type text (`text`)
- `key_press` - Press and release key (`key`)
- `key_down` - Press key
- `key_up` - Release key
- `key_combo` - Key combination (`modifiers`: list, `key`)

### Gamepad Actions

- `gamepad_press` - Press button
- `gamepad_release` - Release button
- `gamepad_left_stick` - Set left stick (`x`, `y`: 0-255)
- `gamepad_right_stick` - Set right stick
- `gamepad_triggers` - Set triggers (`left`, `right`: 0-255)

## Building

```bash
cd usehid-python
maturin develop
```

## License

MIT
oomllama 0.5.0
OomLlama - Smart LLM routing with TIBET provenance. Route queries to the right model, track everything.
License: MIT
Requires Python: >=3.10
Keywords: ai, audit, inference, llama, llm, ml, ollama, provenance, qwen, routing, tibet

# OomLlama

Smart LLM routing with TIBET provenance. Route queries to the right model, track everything.

## Installation

```bash
pip install oomllama
```

With TIBET provenance:
```bash
pip install oomllama[tibet]
```

## Quick Start

```python
from oomllama import OomLlama

# Simple generation
llm = OomLlama()
response = llm.generate("Hello!")

# With specific model
response = llm.generate("Complex question", model="qwen2.5:32b")

# Auto-routing (picks best model for the query)
llm = OomLlama(auto_route=True)
response = llm.generate("Write a Python function")  # Routes to code model
```

## Smart Routing

OomLlama automatically selects the best model based on your query:

```python
from oomllama import OomLlama, ModelRouter

llm = OomLlama(auto_route=True)

# Code query → routes to code-capable model
llm.generate("Write a binary search function")

# Simple query → routes to fast model
llm.generate("What is 2+2?")

# Complex query → routes to reasoning model
llm.generate("Explain quantum entanglement in detail...")
```

## TIBET Provenance

Track every LLM call with cryptographic provenance:

```python
from oomllama import OomLlama
from tibet_core import Provider

# Enable TIBET tracking
tibet = Provider(actor="jis:company:my_app")
llm = OomLlama(tibet=tibet)

# All calls now create provenance tokens
response = llm.generate("Summarize this document")

# Audit trail
for token in tibet.find(action="llm_generate"):
    print(f"{token.timestamp}: {token.erin['model']}")
    print(f"  Reason: {token.erachter}")
```

## CLI Usage

```bash
# Generate text
oomllama gen "Hello, how are you?"

# Auto-route
oomllama gen --auto "Write a Python web scraper"

# Interactive chat
oomllama chat -m qwen2.5:7b

# List models
oomllama list

# Check status
oomllama status
```

## Configuration

```python
from oomllama import OomLlama

llm = OomLlama(
    model="qwen2.5:7b",           # Default model
    ollama_url="http://localhost:11434",  # Ollama API
    auto_route=True,              # Enable smart routing
    system_prompt="You are helpful."  # Default system prompt
)

# Set defaults
llm.set_defaults(
    temperature=0.8,
    max_tokens=1024
)
```

## Custom Model Router

```python
from oomllama import OomLlama, ModelRouter, ModelConfig, ModelCapability

# Define your models
router = ModelRouter([
    ModelConfig(
        name="my-model:7b",
        size="7b",
        capabilities=[ModelCapability.CODE, ModelCapability.FAST],
        priority=30
    ),
])

llm = OomLlama(router=router, auto_route=True)
```

## Remote Ollama

```python
# Connect to remote GPU server
llm = OomLlama(ollama_url="http://192.168.4.85:11434")
```

## Requirements

- Python 3.10+
- [Ollama](https://ollama.ai) running locally or remotely

## License

MIT - Humotica

## Links

- [Humotica](https://humotica.com)
- [tibet-core](https://github.com/Humotica/tibet-core)
- [Ollama](https://ollama.ai)