This project is a learning exercise on learning LLM agents using AutoGen.

Examples of items to explore:

• How to use agents to solve a task.
• What are the limitations? For example, do we need GPT-4, or could a simpler (and cheaper) model do the job?
• How many tokens do we consume for the agents and the behind-the-scenes processing (the selection and coordination of the agents)?

If you haven't done so yet, prepare the environment. If you have already prepared the environment, activate it with source venv/bin/activate.

Each notebook is a self-contained experiment. You can run them in any order.

You may need to select a Python kernel the first time you open a notebook. Choose the kernel from the virtual environment you created.

• Sequential chat: Given a starting and ending number, select math operators (agents) to transform the starting number into the ending number.

Create a file named .env in the root of the project with the following content:

OPENAI_API_KEY=your-openai-api-key

This file is in the .gitignore. It will never be committed to the repository.

This is a one-time step. If you have already done this, just activate the virtual environment with source venv/bin/activate.

Run the following commands to create a virtual environment and install the required packages.

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt