Graph-based Agent Memory
Graph-based Agent Memory Research summary — May 29, 2026. Sources: arXiv:2602.05665, arXiv:2604.12285, arXiv:2601.03236 Why Graph-based Agent Memory? Traditional LLM agents hit three fundamental walls: knowledge cutoff (static training data), tool incompetence (can’t efficiently learn new tools at runtime), and performance saturation (agents repeat the same errors because they can’t accumulate experience). Memory solves this — but how you store memory matters enormously. Simple memory approaches (flat token windows, vector databases, key-value stores) can retrieve semantically similar content but struggle with relational reasoning — questions like “what changed between these two events?” or “how does concept A relate to concept B via C?” Graphs solve this by natively encoding relationships, hierarchy, and causality. ...