title: Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
summary: 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
  LOCOMO benchmark, we systematically compare our approaches against six baseline
  categories: (i) established memory-augmented systems, (ii) retrieval-augmented
  generation (RAG) with varying chunk sizes and k-values, (iii) a full-context
  approach that processes the entire conversation history, (iv) an open-source
  memory solution, (v) a proprietary model system, and (vi) a dedicated memory
  management platform. Empirical results show 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
  configuration. Beyond accuracy gains, we also markedly reduce computational
  overhead compared to full-context method. In particular, Mem0 attains a 91%
  lower p95 latency and saves more than 90% token cost, offering a compelling
  balance between advanced reasoning capabilities and practical deployment
  constraints. Our findings highlight critical role of structured, persistent
  memory mechanisms for long-term conversational coherence, paving the way for
  more reliable and efficient LLM-driven AI agents.
paper_authors._id: 681132ccee133b09d9d25a65
paper_authors.name: Prateek Chhikara
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-30T07:56:27.874Z
paper_authors._id: 63d81cb4635499f2164094bf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1675107437601-noauth.png
paper_authors.fullname: Prateek Chhikara
paper_authors.type: user
paper_authors.user: prateekchhikara
paper_authors._id: 681132ccee133b09d9d25a66
paper_authors.name: Dev Khant
paper_authors._id: 681132ccee133b09d9d25a67
paper_authors.name: Saket Aryan
paper_authors._id: 681132ccee133b09d9d25a68
paper_authors.name: Taranjeet Singh
paper_authors._id: 681132ccee133b09d9d25a69
paper_authors.name: Deshraj Yadav
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-04T10:05:24.817Z
paper_authors._id: 64b397bf93679cd00945624e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b397bf93679cd00945624e/yQnWK5uve6pMavzn4bR33.jpeg
paper_authors.fullname: Deshraj Yadav
paper_authors.type: user
paper_authors.user: deshrajdry
paper_title: Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
paper_summary: 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
  LOCOMO benchmark, we systematically compare our approaches against six baseline
  categories: (i) established memory-augmented systems, (ii) retrieval-augmented
  generation (RAG) with varying chunk sizes and k-values, (iii) a full-context
  approach that processes the entire conversation history, (iv) an open-source
  memory solution, (v) a proprietary model system, and (vi) a dedicated memory
  management platform. Empirical results show 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
  configuration. Beyond accuracy gains, we also markedly reduce computational
  overhead compared to full-context method. In particular, Mem0 attains a 91%
  lower p95 latency and saves more than 90% token cost, offering a compelling
  balance between advanced reasoning capabilities and practical deployment
  constraints. Our findings highlight critical role of structured, persistent
  memory mechanisms for long-term conversational coherence, paving the way for
  more reliable and efficient LLM-driven AI agents.
paper_ai_summary: Mem0, a memory-centric architecture with graph-based memory, enhances long-term conversational coherence in LLMs by efficiently extracting, consolidating, and retrieving information, outperforming existing memory systems in terms of accuracy and computational efficiency.
paper_ai_keywords: Mem0 memory-centric architecture graph-based memory salient information conversational elements LOCOMO benchmark memory-augmented systems retrieval-augmented generation full-context approach memory solution model system memory management platform single-hop temporal multi-hop open-domain LLM-as-a-Judge reasoning capabilities deployment constraints structured memory persistent memory