FERNme Memory

---

✨ The one-paragraph pitch

Most agent memory is written by an LLM on every turn (expensive, hallucination-prone), evaluated on question-answering (not actions), and assumes a single user. FERNme is built for the opposite world: agents that act for many people, in any domain. It starts where agents already act today, websites, and builds a user-owned personalization model the person can inspect and control. Each user is a sparse, fuzzily-weighted node in a per-site graph; the graph also supports opt-in typed entities with labeled, Hebbian-weighted relations while the deterministic write/recall core stays at zero LLM calls. Optional propose-only enrichment can add connection suggestions powered by the agent you already use or a model you choose; nothing lands in memory truth until a human accepts it. Retrieval is spreading activation, and the prompt-facing card stores only deviations from a population prior.

---

What it is / is not

FERNme is	FERNme is not
A user-owned personalization memory layer for agents	A generic transcript store
A fuzzy graph of preferences, habits, style, constraints, and outcomes	A folder of chat logs with search
Deterministic-first memory writes with optional LLM enrichment	An LLM extraction call on every turn
A bounded prompt card that stays small as memory grows	Full-history injection
An inspectable and editable user model	Hidden behavioral profiling
Consent-first, per-site, default-deny sharing	Cross-site surveillance

---

🎯 Why FERNme (the strong points)