Multi-Agent System for Science, Made by Cosmologists, Powered by AG2.
Try cmbagent on HuggingFace!
Check our demo videos on YouTube!
Join our Discord Server to ask all your questions!
This is open-source research-ready software.
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Check the demo notebooks.
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Best performances are obtained with top-scoring models.
We emphasize that cmbagent is under active development and apologize for any bugs.
The backbone of cmbagent is AG2. Please star the AG2 repo ⭐ and cite Wu et al (2023)!
Cmbagent acts according to a Planning and Control strategy with no human-in-the-loop.
You give a task to solve, then:
Planning
- A plan is designed from a conversation between a planner and a plan reviewer.
- Once the number of feedbacks (reviews) is exhausted the plan is recorded in context and cmbagent switches to control.
Control
- The plan is executed step-by-step.
- Sub-tasks are handed over to a single agent in each step.
With Python 3.12 or above:
python3 -m venv cmbagent_env
source cmbagent_env/bin/activate
pip install cmbagentGo ahead and launch the GUI:
cmbagent runSee below if you need to run in terminal, notebooks etc.
git clone https://github.com/CMBAgents/cmbagent.git
cd cmbagent
python3 -m venv cmbagent_env
source cmbagent_env/bin/activate
pip install -e .You can then open the folder in your VSCode/Cursor/Emacs/... and work on the source code.
We assume you are in the virtual environment where you installed cmbagent.
Here is a one-liner you can run in terminal:
python -c "import cmbagent; task='''Draw two random numbers and give me their sum'''; results=cmbagent.one_shot(task, agent='engineer', engineer_model='gpt-4o-mini');"If you want to run the notebooks, first create the ipykernel (assuming your virtual environment is called cmbagent_env):
python -m ipykernel install --user --name cmbagent_env --display-name "Python (cmbagent_env)"Then launch jupyterlab:
jupyter-labSelect the cmbagent kernel, and run the the notebook.
Before you can use cmbagent, you need to set your OpenAI API key as an environment variable. Do this in a terminal, before launching Jupyter-lab.
For Unix-based systems (Linux, macOS), do:
export OPENAI_API_KEY="sk-..." ## mandatory for the RAG agents
export ANTHROPIC_API_KEY="sk-..." ## optional
export GEMINI_API_KEY="AI...." ## optional (paste in your bashrc or zshrc file, if possible.)
For Windows, use WSL and the same command.
By default, cmbagent uses models from oai/anthropic/google. If you want to pick different LLMs, just adapat agent_llm_configs as above, or the default_agent_llm_configs in utils.py.
You can run the cmbagent GUI in a docker container. You may need sudo permission to run docker, or follow the instructions of this link. To build the docker image run:
docker build -t cmbagent .To run the cmbagent GUI:
docker run -p 8501:8501 --rm cmbagentThat command exposes the default streamlit port 8501, change it to use a different port. You can mount additional volumes to share data with the docker container using the -v flag.
If you want to enter the docker container in interactive mode to use cmbagent without the GUI, run:
docker run --rm -it cmbagent bash @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.