semvec/pypi-packages huggingface dataset:
pytelmbot 0.1.3
A collection of utilities to use for automated embedded testing
License: MIT
Requires Python: >=3.7

# pytelmbot

A collection of utilities to use for automated embedded testing

Designed with pyserial in mind, but could be used for others

## Installation

Install the latest version of the package with:

```sh
pip install pytelmbot
```

## Examples

Example scripts are available in the source distribution or on GitHub.
To install the dependencies required to run the examples, use:

```sh
pip install pytelmbot[examples]
```

## SDWire

Support for managing files on a usb-connected SDWire device by [3MDEB](https://shop.3mdeb.com/shop/open-source-hardware/sdwire/)

### Usage

```python
from pytelmbot import SDWire

# List the available devices to find device-name
SDWire.list_devices() 

sdw = SDWire(device-name)

# Select the Test Server to access files
sdw.select_ts()

# Write a local file to the SD card at its root 
sdw.write_file('./testing/test.txt', '')        

# Copy a file on the SD card to another location
sdw.copy_file('test.txt', 'test_copy.txt')

# Rename a file on the SD card 
sdw.rename_file('test.txt', 'test_renamed.txt')

# Get a file from the SD card and store at a local path
sdw.get_file('test_renamed.txt', './testing')

# Delete a file on the SD card
sdw.delete_file('test_renamed.txt')

# Select the Device Under Test to give the device access
sdw.select_dut()                                 

# Close the SDWire connection
sdw.close()
```
test-sdk-deploy-1748518084 0.1.0
A Python SDK for logging and visualizing OpenAI agent interactions
License: MIT
Requires Python: >=3.10
Keywords: openai, ai, agents, logging, visualization, sdk, cli, dashboard

# Agensight SDK

A simple SDK for tracing and evaluating AI agent workflows.

## Installation

```bash
pip install --upgrade agensight
```

## Quick Start

```python
from agensight import init, trace, span

# Initialize Agensight
init(name="my-agent")

# Add tracing to your functions
@trace("my_workflow")
def my_function():
    @span()
    def my_subtask():
        # Your code here
        pass
    return my_subtask()
```

## Key Features

### 1. Tracing
- Auto-instrumented tracing of LLM calls
- Function execution tracking
- Agent interaction monitoring
- Performance metrics and token usage analytics

### 2. Evaluation
```python
from agensight.eval.metrics import GEvalEvaluator

# Create custom evaluator
accuracy = GEvalEvaluator(
    name="Accuracy",
    criteria="Evaluate response accuracy",
    threshold=0.7
)

# Use in your code
@span(metrics=[accuracy])
def my_function():
    # Your code here
    pass
```

### 3. Local Storage
- All data stored locally in `.agensight`
- No external data uploads
- Complete privacy and control

## Dashboard

Start the dashboard to view traces and metrics:
```bash
agensight view
```
Visit http://localhost:5001 in your browser

## Documentation

For detailed documentation, visit our [docs](https://pype-db52d533.mintlify.app/introduction).

## Support

Join our [GitHub Discussions](https://github.com/pype-ai/agensight/discussions) for support and feature requests.

## License

MIT License • © 2025 agensight contributors