MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. This design limits interpretability and alignment between query intent and retrieved evidence, leading to suboptimal reasoning accuracy. In this paper, we propose MAGMA, a multi-graph agentic memory architecture that represents each memory item across orthogonal semantic, temporal, causal, and entity graphs. MAGMA formulates retrieval as policy-guided traversal over these relational views, enabling query-adaptive selection and structured context construction. By decoupling memory representation from retrieval logic, MAGMA provides transparent reasoning paths and fine-grained control over retrieval. Experiments on LoCoMo and LongMemEval demonstrate that MAGMA consistently outperforms state-of-the-art agentic memory systems in long-horizon reasoning tasks.
695f67735fa3847525c4200d
Dongming Jiang
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2026-02-23T09:48:02.598Z
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dj
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user
dj220001
695f67735fa3847525c4200e
Yi Li
695f67735fa3847525c4200f
Guanpeng Li
695f67735fa3847525c42010
Bingzhe Li
MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
Memory-Augmented Generation (MAG) extends Large Language Models with external memory to support long-context reasoning, but existing approaches largely rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. This design limits interpretability and alignment between query intent and retrieved evidence, leading to suboptimal reasoning accuracy. In this paper, we propose MAGMA, a multi-graph agentic memory architecture that represents each memory item across orthogonal semantic, temporal, causal, and entity graphs. MAGMA formulates retrieval as policy-guided traversal over these relational views, enabling query-adaptive selection and structured context construction. By decoupling memory representation from retrieval logic, MAGMA provides transparent reasoning paths and fine-grained control over retrieval. Experiments on LoCoMo and LongMemEval demonstrate that MAGMA consistently outperforms state-of-the-art agentic memory systems in long-horizon reasoning tasks.
MAGMA is a multi-graph memory architecture that improves long-context reasoning in language models by separating memory representation from retrieval logic across semantic, temporal, causal, and entity dimensions.
memory-augmented generation large language models external memory long-context reasoning semantic similarity monolithic memory stores interpretability query intent retrieved evidence multi-graph agentic memory relational views policy-guided traversal query-adaptive selection structured context construction transparent reasoning paths fine-grained control long-horizon reasoning tasks