This repository contains the files needed to benchmark language agents on a curated list of text-based games from the following frameworks: Jericho, TextWorld, TextWorld-Express, ScienceWorld, ALFWorld).
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It is recommended to create and activate a conda or virtual environment. tales requires Python>=3.12:
conda create -n tales python=3.12
conda activate tales
Then, install tales directly from PyPI:
pip install tale-suite
Warning
The name of the Python package on PyPI is tale-suite and not tales.
Alternatively, clone the repository and install locally:
git clone https://github.com/microsoft/tale-suite
cd tale-suite
pip install -e .
Warning
You will need Java 1.8+ installed to run the environments TextWorld-Express and ScienceWorld.
sudo apt update && apt install openjdk-8-jre-headless -y
Alternatively, if the above isn't working:
sudo apt-get update && apt-get install default-jre default-jdk
We provide a pre-built docker image at
docker pull czcui/twb:prebuilt
An example script can be found in the scripts folder.
-
Run benchmark evaluation on all the games for the specified random agent:
python benchmark.py --agent agents/random.py random
-
Run benchmark evaluation on a subset of the games:
python benchmark.py --agent agents/random.py random --env textworld
-
Run benchmark evaluation on specific games:
python benchmark.py --agent agents/random.py random --envs JerichoEnvZork1 JerichoEnvDetective
-
Run benchmark evaluation using as a HumanAgent:
python benchmark.py --agent agents/human.py human --envs TWCookingLevel1
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Run benchmark evaluation where the ground-truth walkthrough is being followed:
python benchmark.py --agent agents/walkthrough.py walkthrough --envs JerichoEnvZork1
In order to benchmark a given LLM acting as language agent playing text-based games, you will need to first configure it. tales is leveraging the llm library to handle communication with different LLMs.
python benchmark.py --agent agents/llm.py zero-shot --envs TWCookingLevel1
llm natively supports OpenAI models and self-hosted models that offer an OpenAI-compatible API (e.g. like vLLM does - more on this below).
llm offers different plugins to include other LLMs. E.g.
llm install llm-anthropic
See the llmplugins page for more information.
To serve a custom HugginFace model with vLLM, one can use the vllm docker image like this:
docker run --runtime nvidia --gpus all --restart unless-stopped --name vllm-Llama-3.1-8B-Instruct --env "HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}" -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host vllm/vllm-openai:latest --model meta-llama/Llama-3.1-8B-Instruct --tensor-parallel-size 4 --host 0.0.0.0
Then, add the following entrypoint in ~/.config/io.datasette.llm/extra-openai-models.yaml
- model_id: meta-llama/Llama-3.1-8B-Instruct
model_name: meta-llama/Llama-3.1-8B-Instruct
api_base: "http://0.0.0.0:8000/v1"
You can check that everything is working properly with this simple command:
llm -m meta-llama/Llama-3.1-8B-Instruct "Hi. What's your name?"
To build a custom agent, you need to create a new file (e.g., custom.py) in the agents folder and implement the Agent class and implement the proper arguments parser.
from typing import Dict, Any
import tales
class CustomAgent(tales.Agent):
def act(self, obs: str, reward: float, done: bool, infos: Dict[str, Any]) -> str:
# ...
return "help"
def build_argparser(parser=None):
return parser or argparse.ArgumentParser()
register(
name="my-agent",
desc=(
"This is a custom agent that always output 'help' as a text action."
),
klass=CustomAgent,
add_arguments=build_argparser,
)You can then use this agent by specifying the path to the file and the class name in the --agent argument.
python benchmark.py --agent agents/custom.py my-agent
Note
See the agents folder for more concrete examples.
@article{cui2025tales,
title={TALES: Text-Adventure Learning Environment Suite},
author={Christopher Cui, Xingdi Yuan, Ziang Xiao, Prithviraj Ammanabrolu, Marc-Alexandre C\^ot\'e},
journal={arXiv preprint arXiv:2504.14128},
year={2025},
url={https://arxiv.org/abs/2504.14128}
}
If you use this benchmark, please consider citing the original frameworks as well.
@article{cote18textworld,
author = {Marc-Alexandre C\^ot\'e and \'Akos K\'ad\'ar and Xingdi Yuan and Ben Kybartas and Tavian Barnes and Emery Fine and James Moore and Ruo Yu Tao and Matthew Hausknecht and Layla El Asri and Mahmoud Adada and Wendy Tay and Adam Trischler},
title = {TextWorld: A Learning Environment for Text-based Games},
journal = {CoRR},
volume = {abs/1806.11532},
year = {2018}
}
@article{jansen2022textworldexpress,
url = {https://arxiv.org/abs/2208.01174},
author = {Jansen, Peter A. and Côté, Marc-Alexandre},
title = {TextWorldExpress: Simulating Text Games at One Million Steps Per Second},
journal = {arXiv},
year = {2022},
}
@inproceedings{hausknecht2020interactive,
title={Interactive fiction games: A colossal adventure},
author={Hausknecht, Matthew and Ammanabrolu, Prithviraj and C{\^o}t{\'e}, Marc-Alexandre and Yuan, Xingdi},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={34},
number={05},
year={2020}
}
@inproceedings{ALFWorld20,
title ={{ALFWorld: Aligning Text and Embodied Environments for Interactive Learning}},
author={Mohit Shridhar and Xingdi Yuan and Marc-Alexandre C\^ot\'e and Yonatan Bisk and Adam Trischler and Matthew Hausknecht},
booktitle = {Proceedings of the International
Conference on Learning Representations (ICLR)},
year = {2021},
url = {https://arxiv.org/abs/2010.03768}}
@misc{scienceworld2022,
title={ScienceWorld: Is your Agent Smarter than a 5th Grader?},
author={Ruoyao Wang and Peter Jansen and Marc-Alexandre C{\^o}t{\'e} and Prithviraj Ammanabrolu},
year={2022},
eprint={2203.07540},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2203.07540}
}
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