research-town 0.0.3

multi-agent environment for research community simulation

ResearchTown: Simulator of Human Research Community

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News

• [12/24] We release our ResearchTown paper on arXiv and ResearchBench data on HuggingFace.

Introduction

Research Town is a multi-agent platform designed for studying community-level automatic research. To achieve community-based simulation, it defines:

1. 🤖 Researcher: LLM research agents capable of skills such as reading papers, writing papers, discussing ideas, rebutting arguments, and writing reviews.
2. 🎩 Environments: Multi-agent environments, similar to virtual study rooms, where research agents collaborate on tasks like idea discussion, rebuttal writing, or paper writing.
3. ⚙️ Engines: Finite-state machines that manage agent involvement in environments and determine the next steps after task completion. For instance, engines guide agents coming out of idea discussion environment to paper writing environment and help select suitable agents to work together.

Please consider cite our paper if you find it useful:

@article{yu2024researchtown,
  title={ResearchTown: Simulator of Human Research Community},
  author={Yu, Haofei and Hong, Zhaochen and Cheng, Zirui and Zhu, Kunlun and Xuan, Keyang and Yao, Jinwei and Feng, Tao and You, Jiaxuan},
  journal={arXiv preprint arXiv:2412.17767},
  year={2024}
}

Get started

Install from pip

You can install research-town from pypi to use it as a package:

pip install research-town

Install from scratch

Use a virtual environment, e.g. with anaconda3:

conda create -n research-town python=3.10
conda activate research-town
curl -sSL https://install.python-poetry.org | python3
export PATH="$HOME/.local/bin:$PATH"

Configure environment variables

Environment variables such as OPENAI_API_KEY and database related configs are required to run the code. The recommended way to set all the required variable is

1. Copy the .env.template file into the project root with the name .env.

cp .env.template .env

2. Fill the required environment variables in the .env file.

Running the examples

To run examples provided in the examples:

poetry install
cd examples
python research_town_demo.py

All generated research progress like ideas, proposals will be automatically saved in the database folder you set in the .env file.

Developing

Develop Demo

To develop the demo (both frontend and backend): Set DATABASE_FOLDER_PATH=./sample_data in .env file

cd frontend
npm install
npm start

poetry install -E backend
uvicorn backend.app.main:app --reload

Install dev options

Follow the installation instruction above and then, instead of running python -m pip install -e ., run the following commands:

python -m pip install -e ".[dev]"
mypy --install-types --non-interactive research_town
python -m pip install pre-commit
pre-commit clean
pre-commit install

The installation of pre-commit would avoid formatting error and large file injects into github commits.

New branch for each feature

git checkout -b feature/feature-name and PR to main branch.

Before committing

Run poetry run pytest to make sure all tests pass (this will ensure dynamic typing passed with beartype) and poetry run mypy --config-file pyproject.toml . to check static typing. (You can also run pre-commit run --all-files to run all checks)

Check github action result

Check the github action result to make sure all tests pass. If not, fix the errors and push again.

ResearchBench

To execute ResearchBench experiments, please execute 'research_bench/run_review_eval.sh' script. You can adjust the parameters in the script, using the actual INPUT_PATH.

If you encounter openreview not found error, please install the package by running pip install openreview. If any issues come up regarding requests, please change its version to 2.26.

pip install requests==2.26