v0.1.7
v0.1.7
Mem0Sharp: Long-term memory lifecycle and evaluation upgrade
This release focuses on making Mem0Sharp behave more like a real long-term memory system instead of a simple fact store.
Highlights
- Added recency-aware retrieval and freshness-window tuning for time-sensitive memory recall.
- Added stale-memory forgetting and consolidation to model evolving preferences and outdated facts.
- Improved evaluation coverage with realistic long-horizon and retention scenarios.
- Strengthened behavior-aware memory support for normal, dreaming, random-thoughts, and personal-memory modes.
- Updated docs and published benchmark results to match the current verified run.
What’s new
- Long-term memory lifecycle:
- recency bias during search
- freshness filtering for newer vs. stale facts
- forgetting of outdated or superseded memories
- consolidation for preference drift and memory refinement
- More realistic benchmark scenarios:
- realistic-long-haul
- stale-forget
- stricter threshold testing
- richer multi-session behavioral memory evaluation
- Improved evaluation fidelity:
- benchmark outputs now reflect real long-term memory behavior
- self-test mode remains deterministic and safe for plumbing validation without provider credentials
Verified evaluation results
Fresh run results from the current benchmark matrix:
- realistic-long-haul: 91% accuracy, 100% retrieval hit rate
- stale-forget: 86% accuracy, 94% retrieval hit rate
- behavior-personal-memory: 100% accuracy, 94% retrieval hit rate
- llm-rerank: 100% accuracy, 100% retrieval hit rate
Why this matters
Mem0Sharp now better handles:
- changing user preferences over time
- stale or outdated memories
- long-running agent memory that must remain relevant across sessions
- behavior-specific memory patterns that feel more human and contextual
Full Changelog: v0.1.6...v0.1.7