Remembra can store: - Trade decisions with reasoning ("went long NQ at 21450 because of MACD divergence") - Pattern recognition across sessions ("round number rejections working 72% in low VIX") - Regime context (track when strategies work vs don't) - Entity extraction pulls out tickers, levels, signals automatically

The key is semantic recall - your bot can query "what worked last time we saw this setup?" and get relevant past trades, not just keyword matches.

Self-hosted so your trading data stays private. Happy to help you get set up if you want to try it: docs.remembra.dev

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.

The graph-aware approach lets us do things vector search alone can't - like "find all memories about Project X that involve Person Y" without needing exact keyword matches. Entity relationships are first-class citizens.

On data residency: 100% agree it's underestimated. We've seen teams pass on otherwise-great tools because they can't guarantee where memories live. Single Docker container, SQLite by default, your infrastructure = your data.

Would love to hear how it compares to your current setup. Drop any feedback in our Discord or GitHub issues - we're actively iterating.