• Authors: Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, Tushar Khot
• Paper
• Note: This is preliminary version of our code. The complete code to run all experiments in the paper will be added shortly.

This code uses the OpenAI API, please put your access key in KEY.txt. To run experiments on ALFWorld, and WebShop please install and set up the environments from the source repositories alfworld, webshop respectively. Our code base adds extra functionality on top of ALFWorld, please replace the alfworld/agents/environment/alfred_tw_env.py with the provided in our repository. To run the TextCraft environment, please download the crafting recipes and store them in TextCraft/recipes/.

The simplest way to run our code is to build separate conda environments.

conda create --name adaptllm --file requirements.txt python=3.7
conda activate adaptllm
cd ADaPT

To run experiments on specific environments using ADaPT (Sec 5 of our paper) please run the environment specific files with the default parameters. The decomposition depth 'd' can be set using max_depth variable in all files. See examples below.

python run_alfworld.py
python run_textcraft.py
python run_webshop.py

We thank the authors and contributors of ReAct for their public code release.

Please cite our paper if you use our code in your works:

@article{prasad2023adapt,
      author    = "Prasad, Archiki and Koller, Alexander and Hartmann, Mareike and Clark, Peter and Sabharwal, Ashish and Bansal, Mohit and Khot, Tushar",
      title     = "ADaPT: As-Needed Decomposition and Planning with Language Models",
      journal   = "arXiv",
      year      = "2023",}
}