Multi-Agent System for Science, Made by Cosmologists, Powered by ag2.

Cmbagent acts according to a Planning and Control strategy.

Note: This software is under MIT license. We bear no responsibility for any misuse of this software or its outputs.

Note: Check the demo notebooks.

We emphasize that cmbagent is under active development and apologize for any bugs. We present our work-in-progress in Laverick et al (2024).

If you would like to cite us, please use:

   @misc{Laverick:2024fyh,
      author = "Laverick, Andrew and Surrao, Kristen and Zubeldia, Inigo and Bolliet, Boris and Cranmer, Miles and Lewis, Antony and Sherwin, Blake and Lesgourgues, Julien",
      title = "{Multi-Agent System for Cosmological Parameter Analysis}",
      eprint = "2412.00431",
      archivePrefix = "arXiv",
      primaryClass = "astro-ph.IM",
      month = "11",
      year = "2024"
   }

Our project is funded by the Cambridge Centre for Data-Driven Discovery Accelerate Programme.

We are grateful to Mark Sze for help with ag2.

If you plan to modify the code, follow these steps:

Clone and install our package from GitHub.

git clone https://github.com/CMBAgents/ag2
cd ag2
pip install -e .

cd ..

git clone https://github.com/CMBAgents/cmbagent.git
cd cmbagent
pip install -e .

Before installing cmbagent, creating a virual environment is encouraged:

python -m venv /path/to/your/envs/cmbagent_env
source /path/to/your/envs/cmbagent_env/bin/activate

You can then pip install cmbagent in this fresh environment.

If you are a cosmologist, you need the RAG data to use cmbagent in your cosmology work.

Before installation, make sure you do not have any other autogen/pyautogen/ag2 versions installed. You may need to uninstall these packages before installing cmbagent.

Do this:

export CMBAGENT_DATA=/where/you/want/the/data

Note that you need to set the CMBAGENT_DATA environment variable accordingly before using cmbagent in any future session. Maybe you want to add this to your .bashrc or .zshrc file, or in your activate script.

RAG agents are defined in a generic way. The core of the code is located in cmbagent.py.

To generate a RAG agent, create a .py and .yaml file and place them in the assistants directory. Additionally, create a directory named after the agent and include associated files in the data directory of cmbagent.

Apart from the RAG agents, we have assistant agents (engineer and planner) and a code agent (executor).

All agents inherit from the BaseAgent class. You can find the definition of BaseAgent in the base_agent.py file.

Check the demo notebook.

Before you can use cmbagent, you need to set your OpenAI API key as an environment variable:

For Unix-based systems (Linux, macOS):

export OPENAI_API_KEY="sk-..."

(paste in your bashrc or zshrc file, if possible.)

For Windows:

setx OPENAI_API_KEY "sk-..."

Instantiate the CMBAgent with:

from cmbagent import CMBAgent
cmbagent = CMBAgent()

Define a task as:

task = """
Assume Planck values for cosmological parameters, find the k at which the linear matter power spectrum peaks,
as well as the k at which non-linear perturbations become important.
       """

Solve the task with:

cmbagent.solve(task)

If you request any output, it will be saved in the output directory.