I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian

A new memory system for personal AI assistants replaces vector databases by leveraging the large context windows of modern LLMs. This approach, inspired by Google's memory agent pattern, uses a simple SQLite database to store structured memories from notes in applications like Obsidian. The architecture consists of an IngestAgent that extracts summaries and metadata, a ConsolidateAgent that finds patterns and connections between memories over time, and a QueryAgent that reasons over both raw memories and consolidated insights. By feeding this structured information directly into the model's prompt, the system avoids the complexity of embedding pipelines and vector similarity searches. This method allows the AI to reason directly over semantics, providing more nuanced answers for personal-scale knowledge management.