A Framework for Multi-LLM Environment Simulation

Paper

Introduction

**AgentVerse** is designed to facilitate the deployment of multiple LLM-based agents across various applications, providing frameworks for both task-solving and simulation. Think of it as a dynamic theater where digital actors (agents) perform various roles to collaboratively tackle projects and exhibit their skills.

Features

• Task-solving: Agents collaboratively complete tasks in an automatic multi-agent system, ideal for software development and consulting systems.
• Simulation: Users can set up environments to observe agent interactions or behaviors, perfect for game development and behavioral research.

Getting Started

Installation

You can install AgentVerse using the following commands:

git clone https://github.com/OpenBMBAgentVerse.git --depth 1
cd AgentVerse
pip install -e .

For local models like LLaMA, install additional dependencies:

pip install -r requirements_local.txt

Usage

Here’s how you can run simulations and task-solving examples.

Simulation Example

To create a multi-agent environment such as a classroom scenario:

agentverse-simulation --task simulation/nlp_classroom_9players

For a GUI demonstration, use the following command:

agentverse-simulation-gui --task simulation/nlp_classroom_9players

This will allow you to access the classroom environment at http://127.0.0.1:7860.

Understanding the Code Example

The process of setting up a classroom environment can be likened to organizing a school play where various roles are filled by different actors, and every actor has to wait for their turn to speak based on the script. Here’s how the structure works:

• Environment Setup: Think of this as setting the stage. You create a tasks directory, configure properties like maximum dialogue turns, and describe how the interaction should proceed.
• Agent Configuration: Just like casting the right actors, you specify roles and personalities for your agents, setting their behavior and memory aspects.
• Output Parsing: This is akin to reviewing the scripts. You prepare a parser to interpret the dialogues of agents based on pre-defined prompts.

Troubleshooting

If you encounter issues such as errors while installing dependencies or running simulations, here are some tips:

• Ensure Python version is 3.9 or above. Check it using:

python --version

• Double-check the installation of required packages. Consider reinstalling them if errors persist.
• Clear the cache and restart your server if the GUI doesn’t load properly.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.