AgentVerse

A Framework for Multi-LLM Environment Simulation
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【 Paper 】
【English | Chinese 】
**AgentVerse** is designed to facilitate the deployment of multiple LLM-based agents in various applications. AgentVerse primarily provides two frameworks: **task-solving** and **simulation**.
- Task-solving: This framework assembles multiple agents as an automatic multi-agent system (AgentVerse-Tasksolving, Multi-agent as system) to collaboratively accomplish the corresponding tasks.
Applications: software development system, consulting system, etc.
- Simulation: This framework allows users to set up custom environments to observe behaviors among, or interact with, multiple agents. ⚠️⚠️⚠️ We're refactoring the code. If you require a stable version that exclusively supports simulation framework, you can use release-0.1 branch. Applications: game, social behavior research of LLM-based agents, etc.
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📰 What's New
- [2024/3/17] AgentVerse was introduced in NVIDIA's blog - Building Your First LLM Agent Application.
- [2024/1/17] We're super excited to announce that our paper got accepted at ICLR 2024. More updates will be coming soon!
- [2023/10/17] We're super excited to share our open-source AI community hugging face: AgentVerse. You are able to try out the two simulation applications, NLP Classroom and Prisoner's Dilemma,with your code of the openai API key and the openai organization. Have fun!
- [2023/10/5] Re-factor our codebase to enable the deployment of both simulation and task-solving framework! We have placed the code for Minecraft example in the paper at the minecraft branch. Our tool-using example will soon be updated to the main branch. Stay tuned!
- [2023/8/22] We're excited to share our paper AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents that illustrate the task-solving framework
in detail of AgentVerse.
- [2023/6/5] We are thrilled to present an array of demos, including NLP Classroom, Prisoner Dilemma, Software Design, Database Administrator, and a simple H5 Pokemon Game that enables the interaction with the characters in Pokemon! Try out these demos and have fun!
- [2023/5/1] 🚀 AgentVerse is officially launched!
🗓 Coming Soon
- [x] Code release of our paper
- [x] Add support for local LLM (LLaMA, Vicunna, etc.)
- [ ] Add documentation
- [ ] Support more sophisticated memory for conversation history
Contents
- 📰 What's New
- 🗓 Coming Soon
- Contents
- 🚀 Getting Started
- Installation
- Environment Variables
- Simulation
- Framework Required Modules
- CLI Example
- GUI Example
- Task-Solving
- Framework Required Modules
- CLI Example
- Local Model Support
- vLLM Support
- FSChat Support
- 1. Install the Additional Dependencies
- 2. Launch the Local Server
- 3. Modify the Config File
- AgentVerse Showcases
- Simulation Showcases
- Task-Solving Showcases
- 🌟 Join Us!
- Leaders
- Contributors
- How Can You Contribute?
- Social Media and Community
- Star History
- Citation
- Contact
🚀 Getting Started
Installation
**Manually Install (Recommended!)**
**Make sure you have Python >= 3.9**
If you want to use AgentVerse with local models such as LLaMA, you need to additionally install some other dependencies:
**Install with pip**
Or you can install through pip
Environment Variables
You need to export your OpenAI API key as follows：
If you want use Azure OpenAI services, please export your Azure OpenAI key and OpenAI API base as follows：
Simulation
Framework Required Modules
CLI Example
You can create a multi-agent environments provided by us. Using the classroom scenario as an example. In this scenario, there are nine agents, one playing the role of a professor and the other eight as students.