Benjamin Batorsky
Ben is the Lead Data Scientist at GoGuardian, working on text-based ML pipelines for student safety. Previously, he led Data Science teams in academia (Northeastern University, MIT) and industry (ThriveHive). He obtained his Masters in Public Health (MPH) from Johns Hopkins and his PhD in Policy Analysis from the Pardee RAND Graduate School. Since 2014, he has been working in data science for government, academia and industry. His major focus has been on Natural Language Processing (NLP) technology and applications. Throughout his career, he has pursued opportunities to contribute to the larger data science community. He has presented his work at conferences, published articles, taught courses in data science and NLP, and is co-organizer of the Boston chapter of PyData. He also contributes to volunteer projects applying data science tools for public good.
Session
The popularity of agent-based workflows has led to a proliferation of frameworks, each representing different design philosophies. At the core of each framework is a similar set of components; memory, tools and “planning”. By understanding these components, it becomes easier to experiment with different frameworks. In this talk, we will talk about these components and then see how they are implemented in three frameworks: LangGraph, Pydantic.AI and LlamaBot. Our use case will be agent-based search, where our agent will respond to a user query based on a knowledge base. We’ll see how each handles this simple workflow and discuss advantages and disadvantages to these different approaches.