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Membase Reports 93% Agent-Memory Scores

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Membase Reports 93% Agent-Memory Scores
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// 1h agoBENCHMARK RESULT

Membase Reports 93% Agent-Memory Scores

Membase is an evidence-centered memory system for AI agents, organizing episodic, semantic, and procedural memory. It reports scores of 93.12% on LoCoMo, 92.60% on LongMemEval-S, and 92.20% on DMR.

// ANALYSIS

Treating memory as an evidence layer—not merely a vector store—could make long-running agents more reliable and debuggable, though these scores need apples-to-apples evaluation details.

  • –Separating episodic, semantic, and procedural memory maps well to events, facts, and reusable workflows
  • –Evidence preservation can improve provenance, contradiction handling, and error analysis
  • –Strong results across three benchmarks suggest a broadly capable memory architecture
  • –MCP compatibility could make shared memory practical across multiple agent clients
  • –Production adoption will depend on write policies, deletion controls, isolation, and defenses against memory poisoning
// TAGS
membaseagent-memoryagentbenchmarkknowledge-graphmcprag

DISCOVERED

1h ago

2026-10-10

PUBLISHED

2h ago

2026-10-10

RELEVANCE

9/ 10

AUTHOR

Unibase_AI