Retrieval-Augmented Generation (RAG) has become the de-facto method for grounding Large Language Models in enterprise data. But classic RAG quickly reaches its limits: conversations drift, pronouns lose their meaning, long-term state becomes ambiguous, and complex multi-step queries often fail. In this talk, we introduce Structured RAG, a next-generation approach implemented in TypeAgent, an open-source framework originally prototyped by Guido van Rossum. Structured RAG keeps conversation context well-organized, reduces hallucinations, improves retrieval precision, and allows AI agents to handle multi-turn dialogues and complex instructions—while remaining fast and highly scalable.

We walk through the core architecture of Structured RAG, including typed conversations, semantic memory layers, indexing strategies, and automatic disambiguation of historical references. Live demos show how TypeAgent enables long-running AI agents that remain accurate over dozens of turns. Attendees will gain a deep understanding of how to move beyond vanilla RAG and how to incorporate Typed Conversations into real-world Python systems.

I am an active contributor to TypeAgent, an open-source framework initiated by Guido van Rossum for building typed conversational agents and Structured RAG systems. I works on large-scale AI deployments, document understanding pipelines, and knowledge-graph-driven retrieval systems in industrial environments.