OpenBMB / AgentVerse

🤖 AgentVerse 🪐 A Framework for Multi-LLM Environment Simulation --> --> 【 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 branch. Applications: game, social behavior research of LLM-based agents, etc. --- 📰 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: . 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 branch. Our tool-using example will soon be updated to the 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. GUI Example We also provide a local website demo for this environment. You can launch it with After successfully launching the local server, you can visit http://127.0.0.1:7860/ to view the classroom environment. If you want to run the simulation cases with tools (e.g., simulation/nlp_classroom_3players_withtool), you need to install BMTools as follows: This is optional. If you do not install BMTools, the simulation cases without tools can still run normally. Task-Solving Framework Required Modules CLI Example To run the experiments with the task-solving environment proposed in our paper, you can use the following command: To run AgentVerse on a benchmark dataset, you can try To run AgentVerse on a specific problem, you can try To run the tool using cases presented in our paper, i.e., multi-agent using tools such as web browser, Jupyter notebook, bing search, etc., you can first build ToolsServer provided by XAgent. You can follow their instruction to build and run the ToolServer. After building and launching the ToolServer, you can use the following command to run the task-solving cases with tools: We have provided more tasks in that show how multi-agent can use tools to solve problems. Also, you can take a look at for more experiments we have done in our paper. Local Model Support vLLM Support If you want to use vLLM, follow the guide here to install and setup the vLLM server which is used to handle larger inference workloads. Create the following environment variables to connect to the vLLM server: Then modify the in the task config file so that it matches the model name in the vLLM server. For example: FSChat Support This section provides a step-by-step guide to integrate FSChat into AgentVerse. FSChat is a framework that supports local models such as LLaMA, Vicunna, etc. running on your local machine. • Install the Additional Dependencies If you want to use local models such as LLaMA, you need to additionally install some other dependencies: • Launch the Local Server Then modify the and according to your need to launch the local server with the following command: The script will launch a service for Llama 7B chat model. The in AgentVerse currently supports several models including , , , , and . If you wish to integrate ad…