ChemToolAgent

This is the official code repo for the paper ChemToolAgent: The Impact of Tools on Language Agents for Chemistry Problem Solving.

News:

• [2025.03] Our new arXiv version now includes experimental results on SciBench. Check it out!
• [2025.03] ChemAgent is now ChemToolAgent, and the paper title is also updated (previously Tooling or Not Tooling? The Impact of Tools on Language Agents for Chemistry Problem Solving).
• [2025.01] Our work is accepted to NAACL 2025 Findings.

Installation

API Keys

To use the backbone LLMs (GPT-4o and Claude-3.5-Sonnet) and some tools, you need to obtain the API keys first. Please follow the links in api_keys.py to get your keys and insert your keys there.

Environment Setup

conda create -n chemagent python=3.9
pip install -r requirements.txt

# Manually install Uni-Core, following https://github.com/dptech-corp/Uni-Core

Usage

cd to the python_server folder, run:

./start_jupyter_server.sh 8888

Then cd back to the project root folder, and use the following commands to query ChemAgent:

from api_keys import api_keys
from chemagent import ChemAgent

agent = ChemAgent(model='gpt-4o-2024-08-06', api_keys=api_keys)

query = "What is the molecular weight the chemical compound Caffeine."
final_answer, tool_use_chain, conversation, conversation_with_icl = agent.run(query)

You could play the agent in the Jupyter notebook playground.ipynb.

Citation

If our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries.

@article{yu2024chemtoolagent,
    title={ChemToolAgent: The Impact of Tools on Language Agents for Chemistry Problem Solving},
    author={Botao Yu and Frazier N. Baker and Ziru Chen and Garrett Herb and Boyu Gou and Daniel Adu-Ampratwum and Xia Ning and Huan Sun},
    journal={arXiv preprint arXiv:2411.07228},
    year={2024}
}