| Thanks for the response The determinism trade-off is genuinely interesting — auditability over fuzziness is a real design philosophy, not just a limitation. We've been building something that tries to avoid forcing that choice. Engram uses three strategies in parallel: vector embeddings (nomic-embed-text via Ollama, local-first), BM25 keyword, and temporal recency — merged with Reciprocal Rank Fusion. Each result comes back with an explicit similarity score and the tier it came from (working memory / long-term / archived), so the retrieval path is still traceable even when it's fuzzy. We also layer on a graph component similar to yours — entity-relationship extraction that augments top results with connected context. The difference is that graph is additive on top of embedding retrieval rather than the primary mechanism. The place your approach wins clearly is corpus-specific precision. If the graph is built from your actual usage (your JWT/authentication example), tag traversal will reliably surface relationships that vectors would miss or dilute with internet priors. That's a real advantage for execution traces and project memory. Still working through the right defaults for consolidation (when to summarize old working memories vs keep them granular). Curious whether you've thought about memory aging in your model. Repo if curious: github.com/Cartisien/engram (http://github.com/Cartisien/engram) |