# AgentSquare

**Repository Path**: wenwu807/AgentSquare

## Basic Information

- **Project Name**: AgentSquare
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-11-14
- **Last Updated**: 2024-11-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">
<img src="pics/logo1.png" style="width: 20%;height: 10%">
<h1> AgentSquare: Automatic LLM Agent Search In Modular Design Space </h1>
</div>
<div align="center">
  
![Code License](https://img.shields.io/badge/Code%20License-Apache--2.0-blue.svg)
![Python 3.8+](https://img.shields.io/badge/python-3.9.12-blue.svg)

</div>
<div align="center">
  <!-- <a href="#model">Model</a> • -->
  🌐 <a href="https://tsinghua-fib-lab.github.io/AgentSquare_website">Website</a> |
  📃 <a href="https://arxiv.org/abs/2410.06153">Paper</a> |
  
</div>

# AgentSquare
The official implementation for paper [AgentSquare: Automatic LLM Agent Search in Modular Design Space](https://arxiv.org/abs/2410.06153) with code, prompts and results.

<p float="left">
  <img src="pics/demo-v2.gif">
</p>

![intro](pics/intro.png)

## 🎉 News
- [x] [2024.11.07]🔥Provide demos of AgentSquare.
- [x] [2024.10.10]🔥Release the source code and our searched new modules.
- [x] [2024.10.08]🔥Release the full paper [AgentSquare: Automatic LLM Agent Search in Modular Design Space](https://arxiv.org/abs/2410.06153)!

## 🌎 Setup
1. Set up OpenAI API key and store in environment.
```bash
export OPENAI_API_KEY=<YOUR KEY HERE>
```
2. Install dependencies
```bash
git clone https://github.com/tsinghua-fib-lab/AgentSquare.git
conda create -n agentsquare python=3.9.12
conda activate agentsquare
cd AgentSquare
pip install -r requirements.txt
```

## 🚀 Quick Start: Demo with ALFWorld

https://github.com/user-attachments/assets/23090869-8c60-4ee8-98ec-75dd6f4255a0

An exemplar script combining different agent modules to solve the task of ALFworld:
```bash
export ALFWORLD_DATA=<Your path>/AgentSquare/tasks/alfworld
cd tasks/alfworld
sh run.sh or 
python3 alfworld_run.py \
    --planning deps\
    --reasoning cot\
    --tooluse none\
    --memory dilu\
    --model gpt-3.5-turbo-0125 \
```

## 🔎 Run Other Tasks
### Install dependencies
```bash
cd tasks
pip install -r requirements.txt
```

<details>
  
<summary> Webshop </summary>
  
Install `webshop` environment following instructions [here](https://github.com/princeton-nlp/WebShop) and launch the `WebShop` webpage.
```bash
cd tasks/webshop
sh run.sh
```
</details>

<details>

<summary> M3Tooleval </summary>

```bash
cd tasks/m3tooleval
sh run.sh
```
</details>

<details>

<summary> Sciworld </summary>

Install `Sciworld` environment following instructions [here](https://github.com/hkust-nlp/AgentBoard) .
```bash
cd tasks/sciworld/agentboard
python3 eval_main_sci.py \
    --cfg-path ../eval_configs/main_results_all_tasks.yaml     --tasks scienceworld     --wandb     --log_path ../results/gpt-4o-2024-08-06    --project_name evaluate-gpt-4o-2024-08-06     --baseline_dir ../data/baseline_results \
    --model gpt-4o-2024-08-06 \
    --planning none \
    --reasoning cot \
    --tooluse none \
    --memory none \
```
</details>

## 🌟 Modular Design Challenge
We kindly invite you to participate in the modular design challenge by standardizing your LLM agents with our recommended I/O interfaces.  Let's work together to offer a platform for fully exploiting the potential of successful agent designs and consolidating the collective efforts of LLM agent research community! 

### Contribute New Modules
For guidance on standardizing the I/O interfaces of the four types of agent modules, please refer to [module pools](modules), which provides some existing modules, along with a complete interface description available in [module interface description](modules/README.md). [Click here](modules/test_new_modules.md) for a detailed procedure. You can submit your standardized modules through this [link](https://drive.google.com/drive/folders/1CrtW_s3n0-tCJRtUDzaKFWrBid7MuF9v?usp=sharing). The .py file format is preferred, examples can be seen in the `modules` folder. We will check your submission timely, once approved we will cite and acknowledge your works in this repository. 

## 💡 How to Add Your Own Task
You can refer to the `workflow.py` to integrate it with your encapsulated tasks, just like in `tasks/alfworld`.

## Citations
Please considering citing our paper and staring this repo if you use AgentSquare and find it useful, thanks! Feel free to contact fenglixu@tsinghua.edu.cn or open an issue if you have any question.

```bibtex
@article{shang2024agentsquare,
  title={AgentSquare: Automatic LLM Agent Search in Modular Design Space},
  author={Shang, Yu and Li, Yu and Zhao, Keyu and Ma, Likai and Liu, Jiahe and Xu, Fengli and Li, Yong},
  journal={arXiv preprint arXiv:2410.06153},
  year={2024}
}
```