Framework Required Modules

- agentverse 
  - agents
    - simulation_agent
  - environments
    - simulation_env

Framework Required Modules

- agentverse 
  - agents
    - simulation_env
  - environments
    - tasksolving_env

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

🚀 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

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

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

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 ```shell

AgentVerse Showcases

Simulation Showcases

Refer to simulation showcases

Task-Solving Showcases

Refer to tasksolving showcases

Environment Variables

You need to export your OpenAI API key as follows： ```bash

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](https://github.com/OpenBMB/XAgent). You can follow their [instruction](https://github.com/OpenBMB/XAgent#%EF%B8%8F-build-and-setup-toolserver) 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:

Also, you can take a look at agentverse/tasks/tasksolving for more experiments we have done in our paper.

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.

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：