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.
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
DISCOVERED
1h ago
2026-10-10
PUBLISHED
2h ago
2026-10-10
RELEVANCE
AUTHOR
Unibase_AI