Explicit: things you tell it to remember Implicit: behavioral patterns extracted from how you work (low confidence, reinforced over time) Synthesized: meta-observations generated during consolidation

The key architectural bet: intelligence at read time, not write time. Most memory systems extract and classify on ingest. Engram stores broadly and invests compute when you query, because that's when you actually know what matters. This is why it scores 80% on LOCOMO (arXiv:2402.17753) while using 30x fewer tokens than full-context retrieval. Stack: TypeScript, SQLite + sqlite-vec, Gemini embeddings by default (any OpenAI-compatible provider works via ENGRAM_LLM_BASE_URL). Zero external dependencies.

Install: npm install -g engram-sdk && engram init Comparison with Mem0, Zep/Graphiti, Letta/MemGPT: https://www.engram.fyi/compare GitHub: https://github.com/tstockham96/engram