Package: agentverse
🤖 AgentVerse 🪐【English |Chinese】AgentVerseis designed to facilitate the deployment of multiple LLM-based agents in various applications. AgentVerse primarily provides two frameworks:task-solvingandsimulation.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 userelease-0.1branch. Applications: game, social behavior research of LLM-based agents, etc.📰 What's New[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 theminecraftbranch. Our tool-using example will soon be updated to themainbranch. Stay tuned![2023/8/22] We're excited to share our paperAgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agentsthat  illistrate the task-solving framework
in detail of AgentVerse.[2023/6/5] We are thrilled to present an array ofdemos, includingNLP Classroom,Prisoner Dilemma,Software Design,Database Administrator, and a simpleH5 Pokemon Gamethat enables the interaction with the characters in Pokemon! Try out these demos and have fun![2023/5/1] 🚀AgentVerseis officially launched!🗓 Coming SoonCode release of ourpaperAdd support for local LLM (LLaMA, Vicunna, etc.)Add documentationSupport more sophisticated memory for conversation historyContents📰 What's New🗓 Coming SoonContents🚀 Getting StartedInstallationSimulation CLI ExampleTask-Solving CLI Example🌟 Join Us!How Can You Contribute?Social Media and CommunityStar HistoryCitationContact🚀 Getting StartedInstallationManually Install (Recommended!)gitclonehttps://github.com/OpenBMB/AgentVerse.git--depth1cdAgentVerse
pythonsetup.pydevelopSome users have reported problems installing theorjsonrequired bygradio. One simple workaround is to install it with Anacondaconda install -c conda-forge orjson.Install with pipOr you can install through pippipinstall-UagentverseYou also need to export your OpenAI API key as follows：# Export your OpenAI API keyexportOPENAI_API_KEY="your_api_key_here"# Or if you are using AzureexportAZURE_OPENAI_API_KEY="your_api_key_here"exportAZURE_OPENAI_API_BASE="your_api_base_here"If you want use Azure OpenAI services, pleas export your Azure OpenAI key and OpenAI API base as follows：exportAZURE_OPENAI_API_KEY="your_api_key_here"exportAZURE_OPENAI_API_BASE="your_api_base_here"If you want to use the tools provided by BMTools, you need to install BMTools as follows:gitclonegit+https://github.com/OpenBMB/BMTools.gitcdBMTools
pipinstall-rrequirements.txt
pythonsetup.pydevelopSimulationFramework Required Modules- agentverse 
  - agents
    - simulation_agent
  - environments
    - simulation_envCLI ExampleYou 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.agentverse-simulation--tasksimulation/nlp_classroom_9playersGUI Example (Local)We also provide a local website demo for this environment. You can launch it withagentverse-simulation-gui--tasksimulation/nlp_classroom_9playersAfter successfully launching the local server, you can visithttp://127.0.0.1:7860/to view the classroom environment.Task-SolvingFramework Required Modules- agentverse 
  - agents
    - simulation_env
  - environments
    - tasksolving_envCLI ExampleTo run the experiments with the task-solving environment proposed in ourpaper, you can use the following command:To run AgentVerse on a benchmark dataset, you can try# Run the Humaneval benchmark using gpt-3.5-turbo (config file `agentverse/tasks/tasksolving/humaneval/gpt-3.5/config.yaml`)agentverse-benchmark--tasktasksolving/humaneval/gpt-3.5--dataset_pathdata/humaneval/test.jsonl--overwriteTo run AgentVerse on a specific problem, you can try# Run a single query (config file `agentverse/tasks/tasksolving/brainstorming/gpt-3.5/config.yaml`). The task is specified in the config file.agentverse-tasksolving--tasktasksolving/brainstormingYou can take a look atagentverse/tasks/tasksolvingfor more experiments we have done in our paper.AgentVerse ShowcasesSimulation ShowcasesRefer tosimulation showcasesTask-Solving ShowcasesRefer totasksolving showcases🌟 Join Us!AgentVerse is on a mission to revolutionize the multi-agent environment for large language models, and we're eagerly looking for passionate collaborators to join us on this exciting journey.LeadersContributorsHow Can You Contribute?Issue and Pull-Request: If you encounter any problems when use AgentVerse, you can propose the issue in English. Beisdes, you can also autonomously ask us to assign issue to you and send the PR (Please follow thePULL_REQUEST_TEMPLATE) after you solve it.Code Development: If you're an engineer, help us refine, optimize, and expand the current framework. We're always looking for talented developers to enhance our existing features and develop new modules.Documentation and Tutorials: If you have a knack for writing, help us improve our documentation, create tutorials, or write blog posts to make AgentVerse more accessible to the broader community.Application Exploration: If you're intrigued by multi-agent applications and are eager to experiment using AgentVerse, we'd be thrilled to support your journey and see what you create!Feedback and Suggestions: Use AgentVerse and provide us with feedback. Your insights can lead to potential improvements and ensure that our framework remains top-notch.Also, if you're passionate about advancing the frontiers of multi-agent applications, become core AgentVerse team members, or are eager to dive deeper into agent research. Please reach outAgentVerse Team, and CC toWeize ChenandYusheng Su. We're keen to welcome motivated individuals like you to our team!Social Media and CommunityTwitter:https://twitter.com/Agentverse71134Discord:https://discord.gg/MjG3vzaT.Hugging Face:https://huggingface.co/spaces/AgentVerse/agentVerse.Star HistoryCitationIf you find this repo helpful, feel free to cite us.@article{chen2023agentverse,
  title={Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents},
  author={Chen, Weize and Su, Yusheng and Zuo, Jingwei and Yang, Cheng and Yuan, Chenfei and Qian, Chen and Chan, Chi-Min and Qin, Yujia and Lu, Yaxi and Xie, Ruobing and others},
  journal={arXiv preprint arXiv:2308.10848},
  year={2023}
}ContactAgentVerse Team:agentverse2@gmail.comProject leaders:Weize Chen:chenweize1998@gmail.comYusheng Su:yushengsu.thu@gmail.com