Memanto: A New Memory Layer for Long-Horizon AI Agents
A new paper on arXiv (2604.22085) introduces Memanto, a universal memory layer for agentic AI that challenges the need for complex knowledge graphs. The system uses a typed semantic memory schema with thirteen predefined categories, automated conflict resolution, and temporal versioning, aiming to reduce computational overhead in persistent multi-session agents.
Key facts
- Paper published on arXiv with ID 2604.22085
- Title: Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
- Memanto is a universal memory layer for agentic AI
- It uses thirteen predefined memory categories
- Includes automated conflict resolution mechanism
- Includes temporal versioning
- Challenges the assumption that knowledge graph complexity is necessary
- Aims to reduce computational overhead in ingestion and retrieval
Entities
Institutions
- arXiv