PubMed is a free search interface for biomedical literature, including citations and abstracts from many life science scientific journals. It is maintained by the National Library of Medicine at the NIH. Yet, most users only interact with it through simple keyword searches. In this hands-on tutorial, we will introduce PubMed as a data source for intelligent biomedical research assistants — and build a Health Research AI Agent using modern agentic AI frameworks such as LangChain, LangGraph, and Model Context Protocol (MCP) with minimum hardware requirements and no key tokens. To ensure compatibility, the agent will run in a Docker container which will host all necessary elements.
Participants will learn how to connect language models to structured biomedical knowledge, design context-aware queries, and containerize the entire system using Docker for maximum portability. By the end, attendees will have a working prototype that can read and reason over PubMed abstracts, summarize findings according to a semantic similarity engine, and assist with literature exploration — all running locally on modest hardware.
Expected Audience: Enthusiasts, researchers, and data scientists interested in AI agents, biomedical text mining, or practical LLM integration.
Prior Knowledge: Python and Docker familiarity; no biomedical background required.
Minimum Hardware Requirements: 8GB RAM (+16GB recommended), 30GB disk space, Docker pre-installed. MacOS, Windows, Linux.
Key Takeaway: How to build a lightweight, reproducible research agent that combines open biomedical data with modern agentic AI frameworks.