semvec/pypi-packages huggingface dataset:
Agentrix 0.0.1
A framework for creating AI agents and agent managers
Requires Python: >=3.6

# Agentic

A simple framework for creating AI agents and agent managers with LLM backends.

## Installation

```bash
pip install agentic
```

## Usage

```python
from agentic import Tool, Agent, ManagerAgent
import openai

# Initialize OpenAI client
client = AzureOpenAI(
    api_key=os.getenv('AZURE_OPENAI_API_KEY'),
    api_version="2024-02-15-preview",
    azure_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT')
)

# Create a simple tool
calculator_tool = Tool(
    name="calculator",
    description="Calculate a mathematical expression",
    function=lambda expression: eval(expression),
    inputs={"expression": ["string", "The math expression to evaluate"]}
)

# Create an agent with the tool
math_agent = Agent(
    name="MathAgent",
    system_prompt="You are a helpful mathematical assistant.",
    llm=client,
    tools=[calculator_tool],
    verbose=True
)

# Use the agent
result = math_agent.go("What is 25 squared plus 13?")
print(result)
```

## Creating a Manager Agent

```python
# Create specialized agents
researcher = Agent("Researcher", "You research facts thoroughly.", client)
analyst = Agent("Analyst", "You analyze data and provide insights.", client)

# Create a manager agent
manager = ManagerAgent(
    name="Manager",
    system_prompt="You coordinate multiple agents to solve complex problems.",
    llm=client,
    agents=[(researcher, "Use for researching facts"), (analyst, "Use for data analysis")],
    parallel=True,
    verbose=True
)

# Use the manager agent
result = manager.go("Research the population of France and analyze its growth trend.")
print(result)
```
dspyce 0.1
The *dspyce* package helps to communicate with dspace interfaces. Currently saf (Simple Archive Format) packages and the RestAPI are supported.
Requires Python: >=3.10

# dspyce
## Table of Contents
1. [Description](#description)
2. [Contents](#contents)
   1. [packages](#packages)
   2. [UML-Diagramm](#uml-diagramm)
3. [Requirements](#requirements)

## Description
The package **dspyce** helps to communicate with dspace interfaces. Currently, 
[saf (Simple Archive Format)](https://github.com/dspace-unimr/dspyce/tree/main/dspyce/saf) packages and the [RestAPI](https://github.com/dspace-unimr/dspyce/tree/main/dspyce/rest) are supported.

## Contents
The **dspyce** packages contains the following classes and packages.

### packages
1. [bitstreams](https://github.com/dspace-unimr/dspyce/tree/main/dspyce/bitstreams) -> The package for managing DSpace bitstream objects.
2. [metadata](https://github.com/dspace-unimr/dspyce/tree/main/dspyce/metadata) -> The package manages the handling of DSpace metadata information.
3. [saf](https://github.com/dspace-unimr/dspyce/tree/main/dspyce/rest) -> The saf packages helps you to create saf packages based on given DSpaceObject objects.
4. [rest](https://github.com/dspace-unimr/dspyce/tree/main/dspyce/saf) -> The rest packages handles the communication with the RestAPI of a given DSpace instance.


## Requirements
Requirements are defined in [requirements.txt](https://github.com/dspace-unimr/dspyce/blob/main/requirements.txt), to use the package a python
version >= 3.10 is necessary.
> python >= 3.10
```shell
pip install -r requirements.txt
```