In this tutorial, we build a self-organizing memory system for an agent that goes beyond storing raw conversation history and instead structures interactions into persistent, meaningful knowledge units. We design the system so that reasoning and memory management are clearly separated, allowing a dedicated component to extract, compress, and organize information. At the same time, the main agent focuses on responding to the user. We use structured storage with SQLite, scene-based grouping, and summary consolidation, and we show how an agent can maintain useful context over long horizons without relying on opaque vector-only retrieval.
SimpleMem addresses the challenge of efficient long-term memory for LLM agents through a three-stage pipeline grounded in Semantic Lossless Compression. It maximizes information density and token utilization, achieving superior F1 scores with minimal token cost.
Phidata is a new framework for creating autonomous assistants that overcome the limitations of traditional large language models by integrating long-term memory, contextual knowledge, and actionable tools.
