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OpenBMB / AgentVerse

🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation
Apache License 2.0
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agent ai gpt gpt-4 llm

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🤖 AgentVerse 🪐

License: Apache2 Python Version Build Code Style: Black HuggingFace Discord

【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.

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📰 What's New

🗓 Coming Soon

Contents

🚀 Getting Started

Installation

Manually Install (Recommended!)

Make sure you have Python >= 3.9

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

If you want to use AgentVerse with local models such as LLaMA, you need to additionally install some other dependencies:

pip install -r requirements_local.txt

Install with pip

Or you can install through pip

pip install -U agentverse

Environment Variables

You need to export your OpenAI API key as follows：

# Export your OpenAI API key
export OPENAI_API_KEY="your_api_key_here"

If you want use Azure OpenAI services, please export your Azure OpenAI key and OpenAI API base as follows：

export AZURE_OPENAI_API_KEY="your_api_key_here"
export AZURE_OPENAI_API_BASE="your_api_base_here"

Simulation

Framework Required Modules

- agentverse 
  - agents
    - simulation_agent
  - environments
    - simulation_env

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.

agentverse-simulation --task simulation/nlp_classroom_9players

GUI Example

We also provide a local website demo for this environment. You can launch it with

agentverse-simulation-gui --task simulation/nlp_classroom_9players

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:

git clone git+https://github.com/OpenBMB/BMTools.git
cd BMTools
pip install -r requirements.txt
python setup.py develop

This is optional. If you do not install BMTools, the simulation cases without tools can still run normally.

Task-Solving

Framework Required Modules

- agentverse 
  - agents
    - simulation_env
  - environments
    - tasksolving_env

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

# Run the Humaneval benchmark using gpt-3.5-turbo (config file `agentverse/tasks/tasksolving/humaneval/gpt-3.5/config.yaml`)
agentverse-benchmark --task tasksolving/humaneval/gpt-3.5 --dataset_path data/humaneval/test.jsonl --overwrite

To 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 --task tasksolving/brainstorming

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:

agentverse-tasksolving --task tasksolving/tool_using/24point

We have provided more tasks in agentverse/tasks/tasksolving/tool_using/ that show how multi-agent can use tools to solve problems.

Also, you can take a look at agentverse/tasks/tasksolving 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:

export VLLM_API_KEY="your_api_key_here"
export VLLM_API_BASE="http://your_vllm_url_here"

Then modify the model in the task config file so that it matches the model name in the vLLM server. For example:

model_type: vllm
model: llama-2-7b-chat-hf

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.

1. Install the Additional Dependencies

If you want to use local models such as LLaMA, you need to additionally install some other dependencies:

pip install -r requirements_local.txt

2. Launch the Local Server

Then modify the MODEL_PATH and MODEL_NAME according to your need to launch the local server with the following command:

bash scripts/run_local_model_server.sh

The script will launch a service for Llama 7B chat model. The MODEL_NAME in AgentVerse currently supports several models including llama-2-7b-chat-hf, llama-2-13b-chat-hf, llama-2-70b-chat-hf, vicuna-7b-v1.5, and vicuna-13b-v1.5. If you wish to integrate additional models that are compatible with FastChat, you need to:

  1. Add the new MODEL_NAME into the LOCAL_LLMS within agentverse/llms/__init__.py. Furthermore, establish
  2. Add the mapping from the new MODEL_NAME to its corresponding Huggingface identifier in the LOCAL_LLMS_MAPPING within the agentverse/llms/__init__.py file.

3. Modify the Config File

In your config file, set the llm_type to local and model to the MODEL_NAME. For example

llm:
  llm_type: local
  model: llama-2-7b-chat-hf
  ...

You can refer to agentverse/tasks/tasksolving/commongen/llama-2-7b-chat-hf/config.yaml for a more detailed example.

AgentVerse Showcases

Simulation Showcases

Refer to simulation showcases

Task-Solving Showcases

Refer to tasksolving 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.

Leaders

Leader Leader

Contributors

Contributor Contributor Contributor Contributor Contributor Contributor Contributor Contributor Contributor Contributor Contributor Contributor

How Can You Contribute?

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 out AgentVerse Team, and CC to Weize Chen and Yusheng Su. We're keen to welcome motivated individuals like you to our team!

Social Media and Community

Star History

Star History Chart

Citation

If 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}
}

Contact

AgentVerse Team: agentverse2@gmail.com

Project leaders: