Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues. We introduce Mem0, a scalable memory-centric architecture that addresses this issue by dynamically extracting, consolidating, and retrieving salient information from ongoing conversations. Building on this foundation, we further propose an enhanced variant that leverages graph-based memory representations to capture complex relational structures among conversational elements. Through comprehensive evaluations on the LOCOMO benchmark, we systematically compare our approaches against six baseline categories. Empirical results demonstrate that our methods consistently outperform all existing memory systems across four question categories: single-hop, temporal, multi-hop, and open-domain. Notably, Mem0 achieves 26% relative improvements in the LLM-as-a-Judge metric over OpenAI, while Mem0 with graph memory achieves around 2% higher overall score than the base Mem0 configuration. Beyond accuracy gains, we also markedly reduce computational overhead compared to the full-context approach. In particular, Mem0 attains a 91% lower p95 latency and saves more than 90% token cost, thereby offering a compelling balance between advanced reasoning capabilities and practical deployment constraints. Our findings highlight the critical role of structured, persistent memory mechanisms for long-term conversational coherence, paving the way for more reliable and efficient LLM-driven AI agents. Code: https://mem0.ai/research.