Remembra uses temporal edges on the knowledge graph. When you store contradictory info ("user prefers Python" then later "user prefers Rust"), we don't just overwrite - we track both with timestamps and can:
1. Return the most recent by default 2. Surface contradictions explicitly when queried 3. Let you query point-in-time ("what did user prefer in January?")
For bad data, we have a few approaches: - Confidence scoring on memories - GDPR-compliant forget() to purge specific memories - Audit logging so you can trace what was stored and when
You're right that memory quality is critical. Our benchmark focus (100% on LoCoMo) is specifically about retrieval accuracy - getting the right memory when you need it, not just any memory that keyword-matches.
Would love to hear how testing goes if you try it.
Treating memory as its own layer (semantic recall, entity relationships, temporal context) seems like a much more realistic direction if we want agents that can actually work on long-running projects.
Curious how Remembra handles conflicting memories or bad data over time. Memory quality might end up being just as important as model quality.