primisai 0.2.0

Nexus is a powerful and flexible Python package for managing AI agents and coordinating complex tasks using LLMs.

Nexus

Nexus is a powerful and flexible Python package for managing AI agents and coordinating complex tasks using LLMs. It provides a robust framework for creating, managing, and interacting with multiple specialized AI agents under the supervision of a central coordinator.

Features

• AI Base Class: A foundational class for AI interactions.
• Agent Class: Extends the AI base class with additional features for specialized tasks.
• Supervisor Class: Manages multiple agents, coordinates tasks, and handles user interactions.
• Debugger Utility: Integrated debugging capabilities for logging and troubleshooting.
• Flexible Configuration: Easy-to-use configuration options for language models and agents.
• Interactive Sessions: Built-in support for interactive chat sessions with the AI system.

Installation

You can install Nexus using pip:

pip install -e .

Quick Start

Here's a simple example to get you started with Nexus:

from nexus.core import AI, Agent, Supervisor
from nexus.utils.debugger import Debugger

# Configure your OpenAI API key
llm_config = {
    "api_key": "your-api-key-here",
    "model": "gpt-4o",
    "base_url": "https://api.openai.com/v1",
}

# Create a supervisor
supervisor = Supervisor("MainSupervisor", llm_config)

# Create and register agents
agent1 = Agent("Agent1", llm_config, system_message="You are a helpful assistant.")
agent2 = Agent("Agent2", llm_config, system_message="You are a creative writer.")

supervisor.register_agent(agent1)
supervisor.register_agent(agent2)

# Start an interactive session
supervisor.display_agent_graph()
supervisor.start_interactive_session()

Documentation

For detailed documentation on each module and class, please refer to the inline docstrings in the source code.

Advanced Usage

Nexus allows for complex interactions between multiple agents. You can create specialized agents for different tasks, register them with a supervisor, and let the supervisor manage the flow of information and task delegation.

# Example of creating a specialized agent with tools
tools = [
    {
        "metadata": {
            "name": "search_tool",
            "description": "Searches the internet for information"
        },
        "tool": some_search_function
    }
]

research_agent = Agent("Researcher", llm_config, tools=tools, system_message="You are a research assistant.", use_tools=True)
supervisor.register_agent(research_agent)

License

This project is licensed under the MIT License.