Fair point, the install instructions at the end were meant as a "here's how to try it if interested" but I can see how it reads as pushy. The core of the post is about the outcome scoring approach itself. Should've led with more depth on the methodology. Thanks for the feedback.

It’s not pushy at all imo

Ran a 4-way comparison test across 200 query-memory pairs:

- Baseline RAG (embedding similarity only): 10%

- RAG + reranker: 20%

- Outcomes only (no reranker): 60%

- RAG + outcome scoring (mature memories with 20+ uses): 60%

"Accuracy" = correct memory ranked #1 for the query. The outcome scoring uses Wilson score lower bound - memories that consistently get positive feedback from the "user" get boosted, ones that fail get demoted.

Test methodology: https://github.com/roampal-ai/roampal/blob/main/dev/benchmar...

You're right, it is a form of tagging technically. The difference is you're already saying "thanks that worked" or "nah that's wrong" anyway. No extra step, it just listens.