250K Tokens, Zero Vector DBs: Google's Memory Agent Revives My Obsidian Notes
My Obsidian AI kept forgetting Alice's Q3 budget approval. Google's Memory Agent Pattern fixed it—no vector databases needed, just raw LLM reasoning over 650 structured memories.
⚡ Key Takeaways
- Google's Memory Agent Pattern uses 250K context to store 650 memories directly, ditching vector DBs for personal notes.
- Three agents (Ingest, Consolidate, Query) in SQLite deliver better recall than embeddings on dates and people.
- This simplicity predicts the end of RAG overkill for indie AI tools—LLMs reason natively now.
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Originally reported by Towards Data Science
