SQL, Vectors, and Graphs for AI Memory: The Relational Case for Memory Architectures

SQL, vectors, and graphs collide in AI memory—and the twist is surprisingly simple: relational databases might outlast flashy stores for persistence. In 2025, the argument to go back to SQL is getting louder as teams chase reliable memory across long conversations ^[1].

What failed—and what works today - Vector databases – semantic recall is strong, with Pinecone and Weaviate, but retrieval can be noisy and lose structure ^[1]. - Graph databases – great for reasoning, yet scaling and maintenance stay tricky ^[1]. - Hybrid systems – flexible, but coordinating vectors, graphs, and relational data adds complexity ^[1]. - Relational databases – the Memori approach uses SQL tables to separate short-term vs long-term memory, store entities, rules, and preferences, promote important facts into permanent memory, and use joins and indexes for retrieval ^[1]. The project comes from Gibson as an open-source memory engine ^[1].

The Relational Pivot Relational memory isn’t nostalgia—it’s practical. Memori aims to give AI agents human-like memory by keeping memories in structured records and leveraging classic SQL querying to recombine context ^[1].

Under the hood A memory search agent shows how retrieval is planned: a SYSTEM_PROMPT guides intent analysis, search parameter extraction, planning, and filtering to surface relevant memories ^[1].

Closing thought Maybe 50 years of “SELECT * FROM reality” still beating the latest semantic embeddings is the plot twist we needed ^[1].

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