- Memora is a scalable memory system designed to improve AI agent productivity on long-horizon tasks by decoupling storage from retrieval.
- It addresses the trade-off between specificity and abstraction in existing memory systems like Mem0 and RAG by using primary abstractions and cue anchors.
- Memora achieves state-of-the-art performance on benchmarks like LoCoMo and LongMemEval, reducing token usage by up to 98% compared to full-context inference.
- The system includes a policy-guided retriever that enables iterative, multi-hop reasoning, mimicking human-like memory recall.