Killed our Vector DB for SQLite BM25 and wrote a 3.6ns Rust sentinel: lessons from multi-agent loops
OpenComplaint524 · reddit · 2026-10-26
A team rebuilding their open-source multi-agent hedge fund after community critique shares three hard-won lessons:
- Factor ontology beats raw metrics: feeding LLMs raw numbers caused polarity hallucinations (framing 65% volatility spikes as bullish). Encoding explicit polarity contracts for 11 quant factors and pre-evaluating signals deterministically dropped polarity hallucinations to 0%.
- Vector DBs are overkill for reflection memory: SQLite FTS5 BM25 retrieved trading post-mortems at 0.35ms with zero API cost vs 200–450ms for embedding retrieval — and keyword matching hit exact historical lessons more reliably than cosine similarity.
- Python can't be your safety wire: a pure Rust sentinel with Struct-of-Arrays layout evaluates stop-losses in 3.63ns per position, letting Python handle slow agent deliberation while Rust watches the portfolio.
The takeaway: LLMs should only do contextual synthesis and debate; everything deterministic belongs in schemas, fast local databases, and fast code.
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