Hande Kafkas
I joined cognee early to help build that engine, and I've been growing with it since. My corner: growth and developer ecosystem, integrations, technical content, partnerships, community. I like the work that sits between building something and getting it into people's hands - understanding the need, driving adoption, and making complex infrastructure accessible. Before cognee, I was an AI engineer consultant and worked in advanced analytics in an enterprise. I took lots of lessons in how enterprise teams actually adopt new tech and that still shapes how I think about developer experience today.
Technical University of Munich (M.Sc.) and Boğaziçi (B.Sc.) alumni, member of 2hearts community. Based in Munich.
Session
RAG-based AI agents fail in production because retrieval without memory is like a conversation with someone who forgets everything you've said. This talk introduces a memory architecture that transforms how you build AI applications with a Python SDK.
Using an open-source Python SDK, cognee, I'll demonstrate how to replace fragile RAG pipelines with a unified memory layer combining knowledge graphs and vector search. You'll see live code showing how 6 lines of Python can give your agents persistent, queryable memory that survives restarts learns and improves with interactions.
We'll build a working agent memory system using cognee, Kuzu, LanceDB, and your choice of LLM provider. The graph and vector layers run embedded with zero infrastructure setup, no database servers required. By the end, you'll understand why the future of AI agents isn't better RAG but better memory.