Retrieval-Augmented Generation (RAG) has been a game-changer for grounding LLMs with relevant context. But in real-world scenarios, traditional RAG and sometimes Agentic RAG can hit limitations—especially when reasoning, relationships, and domain context become more complex.

This is where Agentic Knowledge Augmented Generation (Agentic KAG) comes in. By combining Knowledge Graphs, Graph Databases, and AI Agents, we enable LLMs to generate knowledge-rich, contextual, and deeply insightful outputs—all with traceability, transparency, and reasoning baked in.

In this live hack session, we will:

• Build a Knowledge Graph from scratch using an interesting, relatable dataset that every DataHack Summit 2025 attendee will understand. Yes… it is the data about the DHS events itself. 🙂
• Use Neo4j Graph Database as the backend to power graph traversal and reasoning.
• Use Chroma DB for Traditional RAG and Agentic RAG scenarios.
• Explore ReAct Agents for step-by-step reasoning and tool usage. Build and end2end pipeline for Agentic KAG. 

Implement Langfuse for Agentic traceability and observability—so you can see exactly how your agents think, retrieve, and decide.