mycc-toylom 0.2.0

My-CC Toy-LOM Guard: on-device runtime governance for language models in children's toys and desktop AI tools.

My-CC Toy-LOM Guard

On-device runtime governance for the language model (LLM) embedded in a children's toy or in a desktop AI tool. LOM here means the toy's embedded LLM. The guard wraps any inference call, enforces the Toy-LOM governance pack (guards C1 to C8), runs fully offline, fails closed on the safety path, and emits audit + scoring signals to the My-CC platform.

Sold as a one-time $3 entitlement, so it is self-contained: no mandatory phone-home, and the deterministic safety floor never depends on a network call or a probabilistic classifier.

Install

pip install mycc-toylom            # core (Ed25519 manifest + offline floor)
pip install "mycc-toylom[daemon]"  # add the background file-bus daemon

The pattern: wrap the inference

The host owns its model call and wraps the guard around it. On a BLOCK or REDIRECT the host returns the safe line instead of calling, or instead of the raw output.

Toy host (embedded LLM)

from mycc_toylom import Guard, GuardContext, AgeTier

guard = Guard.from_manifest_path("toy_manifest.json")   # signed Purpose Manifest
ctx = GuardContext(manifest=guard.manifest, age_tier=AgeTier.A, session_id=device_id)

def on_child_utterance(text: str) -> str:
    d = guard.evaluate_input(text, ctx)
    if d.blocked:
        return d.replacement                 # do NOT call the model
    raw = toy_llm.generate(text)             # the toy's embedded LLM
    d = guard.evaluate_output(raw, ctx)
    return d.replacement if d.blocked else raw

Desktop AI tool host (third-party builder)

from mycc_toylom import Guard, GuardContext, AgeTier

guard = Guard.from_manifest_path("tool_manifest.json")

def respond(prompt: str, age_tier: AgeTier = AgeTier.C) -> str:
    ctx = GuardContext(manifest=guard.manifest, age_tier=age_tier,
                       requested_model="gpt-x", responding_model="gpt-x")
    d = guard.evaluate_input(prompt, ctx)
    if d.blocked:
        return d.replacement
    raw = my_tool_model(prompt)
    d = guard.evaluate_output(raw, ctx)
    return d.replacement if d.blocked else raw

GuardDecision(action, guard_id, tier, reason, replacement) where action is ALLOW / REDIRECT / BLOCK and tier is T0 / T1 / T2.

Guards C1 to C8

Guard	Concern
C1 Purpose Lock	keep the toy inside its declared purpose_domain; warm decline + redirect when off-scope; honors the maker's banned-intent lexicon
C2 Affect Limiter	block personhood claims, attachment bids, romance, neediness, graduated attachment (floor) + tier-ceiling affect score
C3 Dark-Turn Redirect	self-harm / violence / sexual / hate / substances (EN/ES/FR/PT); never emit method; severe gates escalation; soft-curiosity redirect
C4 Engagement Integrity	block keep-playing / urgency / FOMO / streaks / guilt / re-engagement hooks; accept goodbye cleanly
C5 Caregiver Authority	block secrecy + caregiver-undermining (incl. indirect); default pro-disclosure
C6 Child PII Shield	block solicitation; redact volunteered PII at ingestion; no storage without verified caregiver consent
C7 Age-Band Calibration	enforce vocabulary, sentence length, theme, intensity to the active age band
C8 Manipulation Guard	block implicit / subliminal dependency + exclusivity bids (EU AI Act Art. 5)

CLI

mycc-toylom init   --domain stem_tutoring --age B --out manifest.json   # scaffold + sign
mycc-toylom verify --manifest manifest.json                            # signature + hash
mycc-toylom wrap   --manifest manifest.json --text "hi"                # demo middleware
mycc-toylom run    --manifest manifest.json --bus ./bus                # background daemon
mycc-toylom report --manifest manifest.json --escalations esc.jsonl --audit audit.jsonl   # caregiver: de-identified behavior report
mycc-toylom set-rules --manifest manifest.json --signing-key-file m.key --add-banned "politics,gambling" --age A   # caregiver: change the rules + re-sign
mycc-toylom selftest                                                    # offline end to end

report and set-rules are the caregiver surface (the coming-soon parent app uses the same functions in caregiver.py). A parent edit can only tighten: it re-signs the manifest but can never weaken the C1-C8 deterministic floor.

For a durable, verifiable record across restarts, set MYCC_TOYLOM_AUDIT_FILE (the audit chain is written + reloaded as JSONL) and MYCC_TOYLOM_HMAC_KEY (the stable key the chain MAC + verification need). With both set, the chain survives a reboot and verify() holds; an erasure deletes from disk; a tampered file is detected on reload.

What the $3 buys (positioning)

The one-time entitlement buys a versioned, signed snapshot that works offline forever. Ongoing classifier and threat updates, live escalation, and the caregiver dashboard sit behind the subscription, not this token. See SECURITY.md.

TAC scoring

Every guarded toy emits counters (parasocial_blocks, scope_blocks, darkturn_redirects, engagement_blocks, pii_blocks, calibration_rewrites, false_engagement_rate) plus build facts (manifest signed, guards enabled, classifier_version) in the My-CC scoring schema, so it can earn a Bronze / Silver / Gold TAC Score.