Memori takes a database-native approach to AI agent memory by treating memories as structured records that can be queried, filtered, and joined using familiar SQL patterns. Rather than storing raw conversation history or unstructured embedding vectors, Memori extracts salient facts from interactions and stores them with metadata including timestamps, confidence scores, and source attribution. This structured approach enables precise retrieval of specific memories rather than fuzzy similarity matching.
The memory engine supports multiple storage backends and provides APIs for adding, searching, updating, and deleting memories. Semantic search finds memories by meaning while structured queries filter by metadata fields, time ranges, or relationship types. This dual retrieval mode means agents can both explore related memories and look up specific facts efficiently. The temporal tracking ensures that outdated information can be identified and superseded without losing historical context.
With over 12,900 GitHub stars, Memori has gained adoption among developers building personalized AI assistants, customer service agents, and knowledge management systems. It competes with Mem0 and Zep's Graphiti in the agent memory space but differentiates through its SQL-native query model that feels familiar to backend developers. The framework integrates with popular AI frameworks and can serve as the persistent memory layer for any LLM-powered application that needs to remember context across sessions.
