hello-agent 0.1.1

Hello World Agent - ReACT Methodology Demonstration System

Hello World Agent

A simple demonstration agent using the ReACT methodology for analyzing and executing tasks.

Quick Install

pip install hello_agent

Prerequisites

• Python 3.8 or higher
• OpenRouter API key (for LLM access)

Installation

1. Install the package:

   pip install hello_agent
   

2. Add your OpenRouter API key to the .env file:

   OPENROUTER_API_KEY=your_api_key_here
   

Usage

The agent can be run in two ways:

1. Using the command-line tool:

   agent --prompt "What is quantum computing?" --task research
   

2. Using Python code:

   from agent.crew import HelloWorldCrew
   
   crew = HelloWorldCrew()
   result = crew.run(prompt="What is quantum computing?", task_type="research")
   

Command Line Arguments

• --prompt: Specify the input prompt (default: "Tell me about yourself")
• --task: Specify the task type: research, execute, analyze, or both (default: both)
• --hitl: Enable human-in-the-loop mode (optional)

Example:

agent --prompt "What is quantum computing?" --task research --hitl

Features

• ReACT Methodology Implementation
• Research Analysis
• Task Execution
• Performance Analysis
• Progress Tracking
• Streaming Responses
• Optional Human-in-the-Loop Mode

Documentation

For detailed documentation and user guides, refer to:

• User Guide
• Templates Guide
• Tools Guide
• Configuration Guide
• Advanced Implementations Guide
• Memory and Storage Guide
• Human-in-the-Loop Guide

Examples

Explore the Examples directory for sample usage scenarios and human-in-the-loop implementations.

Project Structure

hello_world/
├── config/              # Configuration files
│   ├── agents.yaml     # Agent definitions
│   ├── tasks.yaml      # Task definitions
│   └── analysis.yaml   # Analysis rules
├── tools/              # Custom tools
├── docs/               # Documentation
└── examples/           # Example implementations

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with CrewAI
• Powered by OpenRouter