Jupyter Agents: training LLMs to reason with notebooks
The past year has been all about giving LLMs more tools and autonomy to solve more complex and open ended tasks. The goal of the Jupyter Agent is to give the model the ultimate tool: code execution.
A natural way to display multi-step code execution together with reasoning is within a Jupyter Notebook, which consists of code and markdown cells. So we built Jupyter Agent to act as an agent that can execute code directly inside a Jupyter notebook and use this environment to solve data analysis and data science tasks. Think of it like Cursor, but living natively