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Great question on conflicts - this is something we spent a lot of time on.

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.

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