fernme 0.3.3

FERNme — a user-owned, near-zero-LLM memory layer for AI agents (websites today; desktop and mobile on the roadmap).

🌿 FERNme

Fuzzy-Edged Recall Network

Agent personalization memory that models the user, not the transcript.

A user-owned, near-zero-LLM personalization memory layer for AI agents. It turns consented interactions into an inspectable model of each person's preferences, habits, communication style, and constraints — staying token-flat as it grows, while letting people see, edit, delete, and own what agents use to personalize.

Cheap to write · flat to read · interpretable by design · owned by the user

fernme.dev

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✨ 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 (a sale, a booking, a resolved ticket, a completed lesson, a kept appointment — "outcome" is whatever the goal is). 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; edges update by a Hebbian co-occurrence rule with zero LLM calls, retrieval is spreading activation, and the prompt-facing "card" stores only deviations from a population prior. The result: per-turn cost stays flat as a profile grows for years, the user can read and correct what agents use to personalize, and the same engine assembles — only with the user's consent — into a cross-site supernode they fully control.

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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

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🎯 Why FERNme (the strong points)

🪶 Zero-LLM writes	Memory updates are arithmetic on a graph — 0 LLM calls per interaction vs. ~2 for extraction-based memory. No write-time cost, no write-time hallucination.
📉 Flat token cost forever	The prompt card holds ~25 tokens whether it's a visitor's first day or fifth year. A full-history baseline is 77× larger by 120 interactions.
🧠 Strong in every regime	Ties a frequency counter on static recall, beats it 0.72 → 0.13 on drift, and wins on context (0.62 → 0.51). Decay + spreading activation unify stability and adaptivity.
🪟 Glass-box & user-owned	Every preference is visible and editable. People fix what's wrong, delete everything, or export it. Privacy becomes a feature, not a liability.
🏬 Built for outcomes	Evaluated by conversion, not QA. A simulated storefront shows +16% conversion lift vs. non-personalized recommendations.
🧩 User-owned supernode	Sign in across sites → your memories assemble like Lego into one profile you control, default-deny, sensitive data walled off. Not surveillance — the mirror image of it.
🎚 Cost/quality dial	One engine, a memory_mode switch: free key-less pure by default, opt-in gated/offline LLM enrichment when you need Mem0-grade nuance — pay only for the compute you use.
🔐 Verifiable & unlearnable	Every action is logged in a tamper-evident HMAC chain the user can replay to detect any alteration; forget_everywhere wipes the profile and unlearns the person from the population prior — provable right-to-be-forgotten.
🛡 Injection-proof by design	Writes are arithmetic, not LLM extraction, so page/user text can't be "talked into" becoming a belief — tested that injected instructions never enter memory.
🧠 Private collective intelligence	New users benefit from crowd patterns on turn one (cold-start from a population prior), with k-anonymity + differential privacy so no individual leaks. A network-effect moat single-user memories can't have.
🗣 Style & mood memory	Learns how each person communicates (terse/verbose, formal/casual, energy) and tracks their mood with trend detection, so the agent can match tone and notice when someone's frustration is rising — in any domain.
🎯 Outcome-learning, any goal	Memory is reinforced by results — not just recall. record_outcome(success) strengthens what worked and weakens what backfired, where "success" is any goal (purchase, booking, resolved ticket, completed lesson…).
🔍 Explainable	Ask why(user, attr) — get the evidence (observations + good/bad outcomes + dates). No black box.
🔌 Deployable plumbing (research preview; harden per SECURITY.md)	SQLite or Postgres (tested on real PG 16), REST + MCP servers, consent gating, injection-safe writes, proactive triggers — all tested.

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📊 Benchmarks

Honest scope: the numbers below are on synthetic or LLM-authored data, not real users. They validate the mechanism and surface failures; a real-human pilot is the pending next step. The Mem0 (LLM) head-to-head needs an API key and is not yet run.

On LLM-authored people (closest to real, agentic ingestion)

A sample of 16 of 92 third-person profiles (ChatGPT-authored), read as prose only and remembered agentically, then scored against hidden answer keys:

metric	result
preference coverage vs. hidden key	75%
communication style — formality	100%
mood sign / mood arc	94% / 100%
preference drift detected	94%
injection attempts ignored	100%
note → card compression	7.3×

(The "agent" here is an LLM reading prose, so these reflect agent + engine together — the engine is solid; the extraction quality is the agent's.)

Cost, recall, and Pareto (synthetic, multi-seed)

Reproduce: python -m fernme.eval.cost_variance · ... quality · ... drift · ... context · ... ablation · ... pilot

Cost — per-turn memory tokens vs. profile size (5 seeds):

metric	FERNme	baseline
card size	24.9 ± 0.5 tokens (flat)	full history grows linearly
at 120 interactions	1×	77× ± 1.3 larger
LLM calls per write	0	~2 (extraction memory)

Recall quality — precision@5 vs. ground-truth preferences (5 seeds × 40 users):

regime	🌿 FERNme	frequency	recency
static recall	0.74	0.74	0.47
drift (taste shifts)	0.72 ✅	0.13 ❌	0.59
context (precision@3)	0.62 ✅	0.51 (blind)	—

The headline: FERNme is the only method strong everywhere. Frequency can't forget (fails drift); recency is noisy (fails static). FERNme's decay + spreading activation get both.

Cold-start ablation — population prior gives +0.06 precision@5 at turns 1–3, washing out by turn 10 (a real but modest, cold-start-only benefit).

Cost / quality Pareto (python -m fernme.eval.pareto) — measured FERNme recall & tokens, modeled LLM nuance & price (assumptions in-file). Per 1,000 interactions:

strategy	quality	$/1k	vs Mem0
FERNme-pure	0.52	$0.008	122× cheaper
FERNme+gated	0.66	$0.023	42× cheaper
FERNme+offline	0.73	$0.104	9× cheaper
full-history@120	0.82	$0.59 (grows)	—
Mem0-style	0.82	$0.95	1×

FERNme+gated/offline sit on the efficient knee: ~80–90% of the LLM-ceiling quality at 1–2 orders of magnitude less cost. (Modeled assumptions; shape is the point.)

Simulated outcome pilot — fake storefront, learn-from-behavior shoppers: +16% relative conversion lift over a popularity baseline; tied at visit 1 (cold start), pulling ahead as it learns, recovering through a mid-pilot taste drift.

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🎚 Memory modes (one engine, a cost/quality dial)

FERNme ships one core with a deployment-level switch — FernService(memory_mode=...). The default is free, key-less, and tested; LLM modes are opt-in and pluggable.

mode	LLM use	cost	status
pure (default)	none	cheapest, flat	✅ tested, key-less
gated	one small call only on novel free-text	~tiny	🧪 experimental — needs a model
offline	batched consolidate() enrichment, off the hot path	~tiny, amortized	🧪 experimental — needs a model

• A pluggable tagger (tagging.py) does the LLM work; you pass llm_fn, optionally constrained to a controlled vocabulary (the real consistency lever across models).
• The hot write path stays LLM-free in every mode; gated spends a call only when the deterministic mapping finds nothing, and svc.llm_calls counts every invocation for cost transparency.
• See the cost/quality Pareto above for where each mode lands. Honest note: the gated/ offline quality is modeled until run against a real model — the wiring is tested here with a mock LLM, not validated for quality.

🧭 The 9 leapfrog dimensions (status)

FERNme's edge isn't the mechanism (that's now a crowded 2026 category) — it's competing on dimensions single-user, vendor-owned, recall-optimized systems structurally can't.

#	Dimension	Status
9	Communication-style & mood memory	✅ built + tested
2	Outcome-learning for any goal (reinforce on results)	✅ built + tested
8	Explainable provenance (why)	✅ built + tested
1	Private collective priors (network-effect cold-start; k-anonymity + bounded-mean DP)	✅ built + tested
4	Verifiable, cryptographic data ownership (tamper-evident HMAC chain, cascading unlearning)	✅ built + tested
7	Multi-timescale memory (fast context vs. slow identity)	✅ built + tested
6	Self-tuning forgetting (learn decay from outcomes; adapts to drift)	✅ built + tested
5	Injection-resistant by construction (deterministic writes can't be talked into beliefs)	✅ built + tested
3	Open user-owned memory protocol (portable across any agent, with consent)	◑ spec stage

These are deliberately the things HippoGraph et al. can't follow: they're single-user (no collective priors), vendor-owned (no user-owned protocol), and recall-optimized (no outcome loop). Built in honest, tested slices — research-dependent ones are marked.

🏗 Architecture

flowchart TD
    V[Visitor on a website] -->|prompt + action| API[FERNme Service]
    API --> CONSENT{consent?}
    CONSENT -->|no| STOP[blocked]
    CONSENT -->|yes| ENGINE
    subgraph ENGINE[Engine - no LLM in the write path]
      W[Hebbian write + decay] --> G[(Per-site preference graph<br/>fuzzy 0-9 edges)]
      G --> R[Spreading-activation retrieval]
      R --> CARD[Token-minimal card ~25 tok]
      PRIOR[Population prior<br/>differential encoding] --> R
    end
    CARD --> AGENT[Agent: recommend / act]
    G --> CAB[(Cabinet: raw event log)]
    API --> STORE[(SQLite or Postgres<br/>multi-tenant)]
    API --> GLASS[🪟 Glass-box editor]
    API -.user signs in.-> SUPER[User-owned Supernode<br/>cross-site, default-deny]

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🧠 How FERNme works (visual walkthrough)

Why FERNme — adaptive local memory instead of expensive RAG/vector retrieval in the loop.	Core principles — near-zero-LLM, deterministic-first, Hebbian, fuzzy, memory cards, action-aware, user-owned.
How memory grows — new event → connect → strengthen → decay → update the card.	Fuzzy Hebbian graph — sparse, weighted (0–9) edges for users, preferences, topics, and goals.
The LLM gate — an exception, not the default; most events are handled deterministically.	Memory card — bounded, interpretable, token-minimal context for the agent.
Action-aware learning — good outcomes strengthen connections, bad outcomes weaken them.	Architecture — ingestion bridge → vocabulary → fuzzy graph → memory card → agent, with LLM fallback only when uncertain.

🚀 Quickstart

pip install -e ".[dev,api]"

python run_demo.py                      # cold-start → learning → glass-box edit
python supernode_demo.py                # one person, three sites, one owned profile
pytest -q                               # 119 passing, 2 skipped

# experiments
python -m fernme.eval.drift               # FERNme beats a frequency counter when tastes change
python -m fernme.eval.pilot               # +16% simulated conversion lift

# run it live
FERNME_API_KEY=secret uvicorn fernme.api.rest:app --port 8077   # REST API (docs at /docs)
open http://localhost:8077/ui                               # glass-box memory editor
open http://localhost:8077/graph                            # your memory as a graph — focus by site / PC / phone
python -m fernme.api.mcp_server                               # MCP server for agents/Claude

🗄 Storage: defaults to ~/.fernme/fernme.db (SQLite). For production use PostgresStore — same interface, tested against a real Postgres 16. Keep SQLite off cloud-synced folders.

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Minimal API example

from fernme.service import FernService

svc = FernService(db_path=":memory:")
svc.consent("shop.example", "elena", True)

svc.observe(
    "shop.example",
    "elena",
    "chat",
    {
        "tags": ["pref:concise", "pref:oat_milk"],
        "text": "Elena prefers concise answers and oat milk.",
    },
)

print(svc.card("shop.example", "elena")["wire"])

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🧱 What's inside

• Engine — saturating Hebbian write (no LLM), ACT-R decay, spreading activation, token-minimal card.
• Population prior — IDF cold-start; differential (deviation-only) storage is enforced by an explicit prune_to_prior pass (redundant edges read through to the prior).
• Stores — SQLiteStore (zero-setup) and PostgresStore (tested vs real PG 16), one interface.
• Ingestion bridge — a per-site catalog (item_id->tags) plus a controlled, namespaced vocabulary (vocabulary.py) that canonicalizes every tag (catalog, free text, or LLM) to one form (pref:, topic:, goal:, context:) so the same concept never drifts across months. Deterministic by default; gated-LLM only for novel free text. This is the product-critical layer — and the foundation a future recursive/region organization would group on.
• The Cabinet — append-only event log with recall() for specific facts.
• Supernode (supernode.py + auth.py) — user-owned cross-site profile, built by sign-in (verified token → opaque person id), default-deny scoped views, sensitive categories walled off.
• Proactive triggers — due-to-reorder + fading-favorite nudges.
• Safety — event tags treated as untrusted data: injection-pattern dropping, size/value caps.
• Interfaces — REST (/observe /card /recall /edit /export /delete /triggers …) + MCP tools + a glass-box web UI (editor at /ui, cross-surface memory graph at /graph — one memory, focusable by site / PC / phone).
• Governance — consent-gated everywhere; export & right-to-be-forgotten built in.

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🔬 How FERNme compares

FERNme is a different category from conversational memories — it is a user-owned personalization graph evaluated by actions, not a transcript store optimized for QA recall. Don't benchmark it only on LoCoMo; that's the wrong axis.

	🌿 FERNme	Mem0	Zep/Graphiti	Letta	MemOS
Write	no LLM	LLM	LLM → KG	LLM-paged	LLM
Retrieval	spreading activation	vector	graph+time	OS paging	hybrid
Eval axis	outcomes	QA	temporal QA	long-horizon	QA
User-owned + glass-box	✅	–	–	–	–
Multi-tenant per-site	✅	passport	–	–	–

Leads on: write cost, interpretability, per-site user-ownership/consent. Honestly behind on: nuanced/causal preferences (LLM extraction wins), benchmark credibility, ecosystem & distribution.

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⚖️ Honest status

✅ Done & tested (119 passing, 2 skipped): engine, SQLite + real-Postgres stores, supernode + sign-in, triggers, safety, REST/MCP, glass-box UI + memory-graph view, and the full results suite above.

🆕 New, opt-in (not yet validated): resolution/temperature decay layer — implemented and unit-tested, off by default (resolution=False). It is not in the results suite above; its efficacy (vs. the current flat decay) still needs the control-vs-treatment harness before any quality claim.

🚧 Still open (genuinely needs the outside world):

• A real-human per-site pilot — only live users close the loop a simulator can't.
• The Mem0 (LLM) head-to-head — harness wired; run locally with OPENAI_API_KEY.
• Embeddings for context→attribute matching; offline LLM catalog enrichment for messy inputs.

Every claim above is backed by a test or a reproducible experiment. Where a result is simulated, it says so — a simulator proves the mechanism, not real-world behavior.

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📁 Layout

fernme/
  core/      graph types · fuzzy 0–9 edges · event record
  write/     event→attr mapping (no LLM) · Hebbian update · decay
  retrieve/  base-level + spreading activation · token-minimal card
  prior/     population prior · differential encoding · IDF cold-start
  store/     sqlite_store · postgres_store (one interface)
  supernode.py · auth.py · triggers.py · safety.py · service.py
  api/       rest.py (FastAPI) · mcp_server.py · web/glassbox.html · web/graph.html
  eval/      simulator · cost · quality · drift · context · ablation · pilot
tests/       119 passing, 2 skipped   ·   *_demo.py walkthroughs

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📜 License & citation

Apache-2.0, © 2026 Acquilab Inc. — see LICENSE and NOTICE. Security notes in SECURITY.md; the name is a working codename (see NAMING.md). If you use FERNme in research, please cite it via CITATION.cff.

_{Research preview. Benchmarks are synthetic or LLM-authored unless stated otherwise.}