​Move beyond standard vector search and build AI agents that remember, reason, and adapt in real time using FalkorDB.

​Webinar Overview

​Most AI agents today suffer from a frustrating case of digital amnesia. Standard Vector RAG is great for finding isolated text snippets, but it falls flat when an agent needs to understand deep relationships, track evolving user context, or remember past interactions. If you want to build truly autonomous, reliable AI, your agents don't just need access to documents—they need a brain.

​To get there, the AI ecosystem is rapidly shifting toward GraphRAG and Agentic Memory.

​In this technical deep-dive, we will explore how combining Knowledge Graphs with FalkorDB enables agents to form a persistent, low-latency memory layer. You’ll learn how to transform unstructured data into an interconnected web of knowledge, allowing your agents to reason across complex relationships and maintain continuous, fluid context over time.

​🛠️ What We’ll Cover

• ​The Amnesia Problem: Why traditional Vector RAG struggles with complex reasoning and long-term agent state.

• ​The Architecture of an AI Brain: How Knowledge Graphs serve as the ultimate substrate for true Agentic Memory.

• ​FalkorDB Under the Hood: Leveraging the world’s fastest graph database to power real-time, ultra-low-latency GraphRAG workflows.

• ​Dynamic Memory Updates: Techniques for allowing LLM agents to autonomously write, update, and prune their own knowledge base during live interactions.

• ​Live Blueprint & Demo: A step-by-step walkthrough of building a memory-mapped AI agent from scratch.

​👥 Who Should Attend?

• ​AI Engineers & Architects looking to upgrade from basic RAG to advanced, production-grade agentic workflows.

• ​Data Scientists & Software Engineers interested in the intersection of Graph Databases and LLMs.

• ​Product Leaders & CTOs building next-gen enterprise AI applications that require high data accuracy and persistent state.

​🎁 Exclusive Takeaway: All live attendees will receive access to a dedicated GitHub repository containing the session's demo code, architectural blueprints, and an implementation guide for FalkorDB.