Kore is different: - Memory decay based on the Ebbinghaus forgetting curve — memories fade unless retrieved, with half-life based on importance (7 days for casual notes, 1 year for critical info) - Auto-importance scoring locally — no LLM call needed - Semantic search in 50+ languages — local sentence-transformers, zero API calls - Memory compression — auto-merges similar memories - Agent namespace isolation — multi-agent safe - Runs fully offline — SQLite + FTS5, FastAPI, no external services
pip install kore-memory[semantic] then kore to start.
Would love feedback on the decay formula and whether the Ebbinghaus approach makes sense for long-running agents.
maxxmini•17m ago
One thing I've found in practice: importance isn't always knowable at write time. Something that seems trivial today becomes critical context a week later. Have you considered a retrieval-based boost where accessing a memory resets or extends its half-life? That way naturally useful memories self-reinforce.
Also curious about the compression/merging strategy — how do you handle contradictory memories (e.g., "user prefers X" followed by "user now prefers Y")?