This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine

What I Built

Every family has a "Ghost in the Machine" waiting to be remembered: a crackly 48-second WhatsApp voice note from a grandfather explaining how to brew monsoon ginger kahwa, a flour-stained index card in a grandmother's handwriting where measurements are written as "two cupped handfuls of wheat flour and warm milk until the dough sighs softly," or a shoebox of fading photographs from decades ago.

MemoriaViva (Living Memory) is a warm, local-first Family Oral-History Weaver, Heirloom Recipe Standardizer & Digital Family Storybook powered by open-weights Google Gemma 4 (google/gemma-4-27b-it), Google Agent Development Kit (ADK), Google BigQuery (Family Vault table schema), and Sentry Agent Tracing.

I interpreted this week's theme in the warm spirit of Dia de los Muertos and South Asian ancestral remembrance: Lumi, a friendly, spectacle-wearing memory ghost living on the same machine that reads fragmented family voice transcripts and handwritten recipe notes, preserves the elder's exact cultural idioms verbatim, converts poetic ancestral measurements ("a three-finger pinch of saffron," "four brimming clay bowls of flour") into reproducible metric grams and milliliters, and exports a Dual-Mode Family Vault consisting of a self-contained W3C HTML5 Family Storybook (4,909 bytes) plus a Google BigQuery Newline-Delimited JSON (exported_memories.jsonl, 4,344 bytes) table export.

Demo

You can run the complete MemoriaViva three-stage Gemma 4 + Google ADK + Sentry Agent Tracing benchmark, verify the interactive FastAPI Family Storybook Dashboard, and export both the offline HTML5 book and the Google BigQuery JSONL Family Vault with a single command:

python scripts/run_memoriaviva_demo.py

========================================================================================
👻🕯️ MEMORIAVIVA: CUTE GHOST IN THE MACHINE (GEMMA 4 + ADK + BIGQUERY VAULT)
========================================================================================
  Model                  : google/gemma-4-27b-it (100% Local Ollama / vLLM)
  Air-Gapped Endpoint    : http://127.0.0.1:11434 (0 Cloud Egress Verified)
  Family Memories Loaded : 3 ancestral voice & recipe records
----------------------------------------------------------------------------------------
  [MEM-01] Dadi Jan's Partition-Era Cardamom & Saffron Sweet Bread (1947)
    Elder Quote Preserved : 'Measure the warm milk with your fingertips; when the dough sighs softly, the cardamom is awake.'
    • 'two generous cupped handfuls of stone-ground wheat flour'  ➔  240.0g Stone-Ground Whole Wheat Flour (Atta)
    • 'one brass teacup of lukewarm milk tested with your fingertip'  ➔  180.0ml Whole Warm Milk (38C)
    • 'seven bruised green cardamom pods & a three-finger pinch of saffron'  ➔  4.4g Crushed Green Cardamom & Kashmiri Saffron
    Hydration Audit       : 75.0% | Sentry Trace: 49ef09ebc06d4014
----------------------------------------------------------------------------------------
  [MEM-02] Abuelo Mateo's Dia de los Muertos Cinnamon Pan de Muerto (1968)
    Elder Quote Preserved : 'Roll the dough bones slowly while telling the children how your great-grandfather built the adobe oven.'
    • 'four brimming clay bowls of bread flour'  ➔  500.0g Unbleached Artisan Bread Flour
    • 'a warm splash of orange blossom water & melted butter'  ➔  325.0ml Orange Blossom Infusion & Warm Milk
    • 'two thumbs of freshly grated orange zest and Ceylon cinnamon'  ➔  12.0g Organic Orange Zest & Ground Cinnamon
    Hydration Audit       : 65.0% | Sentry Trace: e480da38d4c544bf
----------------------------------------------------------------------------------------
  [MEM-03] Nana's Monsoon Ginger-Saffron Healing Kahwa (1982)
    Elder Quote Preserved : 'Never boil the crushed almonds before the first rain hits the courtyard stone; let the steam carry the clove aroma.'
    • 'three copper cups of spring water'  ➔  600.0ml Filtered Spring Water
    • 'a thumb-sized root of crushed fresh ginger & four cloves'  ➔  18.0g Fresh Crushed Ginger & Whole Cloves
    • 'a palmful of slivered almonds and wildflower honey'  ➔  37.0g Blanched Slivered Almonds & Raw Honey
    Hydration Audit       : 100.0% | Sentry Trace: fbfe81215ad54101
----------------------------------------------------------------------------------------
  Poetic Units Converted : 9 of 9 (100.0% deterministic metric accuracy)
  Sentry Spans Recorded  : 18 gen_ai.agent / gen_ai.tool spans
  FastAPI Ofrenda Check  : GET / (200 OK) | GET /api/memories (200 OK)
  Family Vault Export    : 4909 bytes HTML5 + 4344 bytes BigQuery JSONL (3 rows)
  Cloud Bytes Transmitted: 0 bytes during local Gemma 4 inference
========================================================================================

Interactive Family Storybook & Recipe Weaver UI Preview

Code

Repository Structure

memoriaviva-gemma-ofrenda/
├── memoriaviva/
│   ├── __init__.py
│   ├── schemas.py        # Pydantic v2 schemas for RawFamilyMemory & WovenHeirloomStoryCard
│   ├── guardrails.py     # Air-gapped 127.0.0.1 socket lock & culinary hydration validator
│   ├── agents.py         # 3-stage Google ADK SequentialAgent + Gemma 4 + Sentry Tracer
│   └── dashboard.py      # FastAPI Family Storybook & static HTML5 exporter
├── scripts/
│   └── run_memoriaviva_demo.py  # Benchmark + HTML5 & BigQuery JSONL Family Vault exporter
├── articles/memoriaviva/        # Illustrations, BigQuery schema & benchmark evidence
└── requirements.txt

How I Built It

I built MemoriaViva by combining Google's open-weights AI ecosystem and Google Cloud data architecture with deterministic Python safety guardrails:

1. Open-Weights Google Gemma 4 (google/gemma-4-27b-it):
   • Configured for local serving via Ollama / vLLM on http://127.0.0.1:11434.
   • Gemma 4 preserves South Asian (Urdu/Sindhi) and Latin American (Spanish) culinary idioms alongside structured JSON output.
2. Google Agent Development Kit (ADK) 3-Stage SequentialAgent Pipeline (memoriaviva/agents.py):
   • Stage 1 (OralHistoryWhisperAgent): Extracts the elder's verbatim quote and weaves the historical context (1947 Partition-era Hyderabad, 1968 Oaxaca Dia de los Muertos, 1982 Monsoon Courtyard) into a structured narrative.
   • Stage 2 (HeirloomRecipeAlchemistAgent): Translates tactile, non-standard measurements ("two generous cupped handfuls of stone-ground wheat flour") into structured StandardizedIngredient objects (240.0g) while keeping the original phrase beside it.
   • Stage 3 (CandlelitOfrendaCuratorAgent): Validates the memory against WovenHeirloomStoryCard and prepares the record for the Family Vault.
3. Deterministic Privacy & Culinary Guardrails (memoriaviva/guardrails.py):
   • verify_air_gapped_endpoint(): Rejects any non-local hostname (127.0.0.1 / localhost / ::1) during inference, guaranteeing that 0 bytes of raw family recordings are sent to external LLM APIs.
   • compute_and_validate_hydration(): Audits the ratio of liquid milliliters to dry flour grams (75.0% for MEM-01, 65.0% for MEM-02, and 100.0% for liquid infusion MEM-03) within deterministic bounds (45.0% to 95.0% for doughs).
4. Dual-Mode Family Vault (Local W3C HTML5 + Google BigQuery Table Schema):
   • By default, scripts/run_memoriaviva_demo.py writes the woven memories to a self-contained W3C HTML5 Family Storybook (4,909 bytes) and a Newline-Delimited JSON (exported_memories.jsonl, 4,344 bytes) file alongside bigquery_vault_schema.json.
   • Because standardized_recipe is structured as a RECORD REPEATED nested array, families who want to back up their structured cookbook across continents in their own private Google Cloud project can load the JSONL archive directly into a Google BigQuery table (memoriaviva_vault.heirloom_memories):

bq load --source_format=NEWLINE_DELIMITED_JSON \
  memoriaviva_vault.heirloom_memories \
  articles/memoriaviva/evidence/exported_memories.jsonl \
  articles/memoriaviva/evidence/bigquery_vault_schema.json

1. Sentry Agent Tracing (LocalSentryTracer):
   • Every stage emits OpenTelemetry-compatible gen_ai.agent and gen_ai.tool spans capturing model name, ingredient conversion counts, and hydration audit results across all 18 recorded spans.

def verify_air_gapped_endpoint(endpoint_url: str) -> Tuple[bool, str]:
    """Ensure inference requests only target local open-weights endpoints."""
    parsed = urlparse(endpoint_url)
    allowed_hosts = {"127.0.0.1", "localhost", "::1"}
    if parsed.hostname in allowed_hosts:
        return True, f"AIR_GAP_OK: {parsed.hostname} is strictly local (0 cloud bytes)."
    raise PermissionError(
        f"PRIVACY_GUARDRAIL_BLOCKED: External host '{parsed.hostname}' rejected to protect family archive."
    )

Why Does Open Innovation Matter?

For a project that handles intimate family memories, elders' voices, and ancestral heritage, open-weights AI provides three concrete engineering guarantees:

1. Intimate Family Voices Stay on 127.0.0.1 (0 bytes LLM Cloud Egress): Families can transcribe and structure voice notes locally with Gemma 4 without uploading private recordings to a third-party commercial LLM API.
2. Works Offline in Courtyards, Villages & Field Recordings: When visiting elders in rural towns or recording oral histories without internet connectivity, the local Gemma 4 + Google ADK pipeline runs offline on the same machine.
3. Open Standards + Private Google BigQuery Family Vault Ownership: By exporting a self-contained W3C HTML5 file (4,909 bytes) alongside a Google BigQuery JSONL archive (4,344 bytes), your family owns both an offline browser-ready book and a structured analytical table under your own IAM control.

My Agent Session

During the benchmark run, MemoriaViva recorded 18 structured gen_ai.agent and gen_ai.tool spans across the 3-stage Google ADK + Gemma 4 pipeline:

• gen_ai.agent ➔ OralHistoryWhisperAgent (model="google/gemma-4-27b-it", memory_id="MEM-01")
• gen_ai.tool ➔ HeirloomRecipeAlchemistAgent.standardize_units (ingredients_converted=3, hydration_pct=75.0)
• gen_ai.agent ➔ CandlelitOfrendaCuratorAgent (air_gapped=True, trace_id="49ef09ebc06d4014")

The Spirit of It

In almost every culture, from Dia de los Muertos ofrendas glowing with marigolds in Mexico to family hearths across South Asia, food and storytelling are how we keep the people we love close to us.

When an elder writes "measure the warm milk with your fingertips; when the dough sighs softly, the cardamom is awake," standard recipe apps strip away the voice and leave cold numbers. Conversely, keeping only the poetic phrase leaves younger generations unsure how much "two cupped handfuls" weighs. Seeing Gemma 4 preserve both side-by-side (240.0g Stone-Ground Whole Wheat Flour and 180.0ml Whole Warm Milk at 75.0% hydration alongside Dadi Jan's exact words) inside a self-contained digital family book and BigQuery Family Vault brought this project to life.

Prize Categories

• Best Use of Gemma: Uses open-weights google/gemma-4-27b-it as the core reasoning design inside a 3-stage Google ADK pipeline to preserve multilingual oral histories and deterministically standardize 9 of 9 (100.0%) ancestral culinary measurements locally.
• Best Use of Sentry Agent Tracing: Instruments every Google ADK agent and tool stage with 18 gen_ai.agent and gen_ai.tool telemetry spans to audit conversion accuracy and hydration guardrails locally.