On building memory systems for long-horizon reasoning tasks. This is a really important topic, especially for improving coding agents like Claude Code. Current Memory-Augmented Generation approaches rely on semantic similarity over monolithic memory stores. Everything gets entangled: temporal information, causal relationships, entity references. When you retrieve based on semantic similarity alone, you lose the structure that makes reasoning possible. This design limits interpretability and creates misalignment between what the query actually needs and what gets retrieved. This new research introduces MAGMA, a multi-graph agentic memory architecture that represents each memory item across four orthogonal graphs: semantic, temporal, causal, and entity. Key idea: Instead of stuffing everything into one embedding space, separate the different types of relationships into distinct graph structures. Semantic similarity tells you what's topically related. Temporal graphs tell you what happened when. Causal graphs tell you what led to what. Entity graphs tell you who and what are connected. MAGMA formulates retrieval as policy-guided traversal over these relational views. The agent learns to navigate across graphs based on query intent, enabling adaptive selection and structured context construction. By decoupling memory representation from retrieval logic, MAGMA provides transparent reasoning paths. You can actually see why certain memories were retrieved and how they connect to the query. Experiments on LoCoMo and LongMemEval demonstrate that MAGMA consistently outperforms state-of-the-art agentic memory systems in long-horizon reasoning tasks. Why this work matters: As agents handle increasingly complex, long-running tasks, memory becomes the bottleneck. Monolithic retrieval breaks down when you need to reason about sequences of events, cause and effect, or relationships between entities. Multi-graph memory offers a path forward. Paper: Learn to build effective AI Agents in our academy: