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LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation

Research LLM Agents

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Merged summary

TL;DR - LycheeMemory V2 consolidates semantically coherent conversation segments rather than individual turns, reducing the cost of building long-term agent memory while preserving retrieval accuracy.

  • Produces context-independent typed memory records from finalized semantic segments.
  • Uses lightweight structured indexes and query planning for evidence retrieval.
  • Achieves 89.22% on LoCoMo and 92.20% on LongMemEval-S with GPT-4.1-Mini.
  • Versus A-Mem, cuts construction tokens by 86.0% and 75.9% on the respective benchmarks without increasing query-time token usage.

Sources (1)

LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation

arXiv cs.CL Dongfang Li, Zixuan Liu, Junmai Wang, Jiahe Huang, Fuhao Li, Bonian Jia, Baotian Hu, Min Zhang 2026-08-13 arXiv:2608.12990
Public signals Hugging Face upvotes 14
Providers: Hugging Face · Upvotes 14 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-15 14:32:52.601463 UTC

TL;DR - LycheeMemory V2 consolidates semantically coherent conversation segments rather than individual turns, reducing the cost of building long-term agent memory while preserving retrieval accuracy.

  • Produces context-independent typed memory records from finalized semantic segments.
  • Uses lightweight structured indexes and query planning for evidence retrieval.
  • Achieves 89.22% on LoCoMo and 92.20% on LongMemEval-S with GPT-4.1-Mini.
  • Versus A-Mem, cuts construction tokens by 86.0% and 75.9% on the respective benchmarks without increasing query-time token usage.
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