Mem0: Scalable Long-Term Memory for AI Agents

The provided source introduces Mem0 and Mem0g, two novel memory architectures designed to enhance Large Language Models (LLMs) by overcoming their inherent context window limitations and improving long-term conversational coherence. Mem0 focuses on dynamically extracting, consolidating, and retrieving salient information from conversations in natural language text, while Mem0g augments this with graph-based memory representations to capture complex relational structures. The research evaluates these systems against various baselines, including established memory-augmented systems, Retrieval-Augmented Generation (RAG) approaches, and proprietary models, demonstrating superior performance in accuracy across different question types (single-hop, multi-hop, temporal, and open-domain). Furthermore, Mem0 and Mem0g significantly reduce computational overhead and latency compared to full-context processing, highlighting their practical viability for production-ready AI agents requiring persistent and efficient memory. The findings underscore the critical role of structured and dynamic memory mechanisms for enabling more reliable and effective LLM-driven interactions over extended periods.Source: https://arxiv.org/pdf/2504.19413

There are no comments yet.

Please log in to write the first comment.