Python SDK
The primary surface for process engineers and data teams: heats, molds, pours, plans, predictions, windows and audit queries as first-class objects.
A Python and REST surface over the agent loop, OPC UA and MQTT at the edge, DICONDE ingest for inspection media, and a plan object that carries its own process window, confidence and explanation.
Everything an agent proposes is a plan object. A plan knows its process window, its confidence and how to explain itself — and applying it requires an approver identity.
# Ask the pour agent for a bounded setpoint move on cell P-04
from smelteon import Plant
plant = Plant("plant-04", edge="foundry-edge-02")
heat = plant.heats.current(line="ductile-iron-1")
plan = plant.agents.pour.plan(
heat = heat.id,
part = "crankshaft-4cyl-rev-c",
mold_state = plant.agents.mold.state(),
objective = "minimise shrinkage porosity at journal 3",
twin = "castwin:solidification-v7",
)
# Every move is checked against the approved process window first
if plan.within_window and plan.confidence > 0.94:
plant.apply(plan, mode="bounded", approver="metallurgist-on-shift")
else:
plant.escalate(plan, reason=plan.explain())The CLI speaks in heats, molds, pours, windows and approvals — the vocabulary the plant already uses.
The primary surface for process engineers and data teams: heats, molds, pours, plans, predictions, windows and audit queries as first-class objects.
Read the loop, subscribe to gradings, non-conformances and escalations, and push context from your MES or ERP.
OPC UA, MQTT and Modbus at the edge for furnace, molding, pouring and robot integration, with historian ingest for the process record.
X-ray and CT volumes, ADR results, dimensional scans and vision streams ingested as inspection ground truth.
Jetson Orin or Thor beside the PLC and robot controllers, running TensorRT and Holoscan inference for vision, pour and defect models with no inbound cloud dependency.
Triton Inference Server with the model router, RAPIDS ETL for historian data, and the local audit buffer that replays to cloud on reconnect.
Routes each step to the best and cheapest model: fine-tuned open models for high-volume steps, frontier models for metallurgy and safety reasoning.
Requests Castwin simulations for new geometry, alloy or process changes and receives promoted setpoints back into the cell plan.
Read-first connectors to furnace, molding, pouring, inspection, robot and MES systems; write-back only through the approved-window gate.
Append-only local log of every plan, approval, action, override, escalation and grading, replicated to the tenant's audit store.
| System | Protocol | Direction |
|---|---|---|
| Induction & holding furnaces | OPC UA / vendor API | Read, then bounded write |
| Spectrometry & thermal analysis | File drop / API | Read |
| Molding & coremaking lines | OPC UA / PLC tags | Read, then bounded write |
| Auto-pour & ladle systems | Vendor API / PLC | Read, then bounded write |
| HPDC / LPDC cells | OPC UA / vendor API | Read, then bounded write |
| Robot controllers (KUKA, FANUC, ABB) | Vendor API / safety-rated I/O | Read, then bounded motion |
| X-ray, CT, vision, dimensional | DICONDE / vendor SDK | Read |
| Foundry MES & ERP | REST / database | Read and write records |
| Historians | OPC UA / SQL / time-series API | Read |
Every integration ships with a documented failure mode. Write-back is enabled per connector only after a safety review.
The loop runs at the cell. Cloud is for training, validation and fleet updates — never a dependency for control.
Degraded modes fall back to sensing and prediction, never to uncontrolled action. A confused agent watches; it does not experiment.
Plans carry their grounding, citations, confidence and window. If it cannot be explained to a metallurgist, it does not ship.
Model and prompt changes pass golden sets, LLM-as-judge evaluation and twin scenario tests in CI before they reach a plant.
8–24
Camera or detector streams per line
30–120
FPS vision inference per line
12–40
Models served per factory
< 60s
Line reschedule target
[ASPIRATIONAL] Engineering targets from Smelteon's GPU utilization plan.
Docs · deployment
Hardware requirements, network posture, connector setup and the shadow-mode checklist.
Docs · concepts
Heats, molds, pours, plans, windows, gradings and genealogy — the object model behind the API.
Docs · autonomy
Golden-set gates, process-window authoring, safety review and rollback configuration.
Security
Isolation, alloy IP protection, grounding, evaluation and the immutable audit log.
Python is the primary SDK, with a REST surface and webhooks for everything else. At the edge, integration is over OPC UA, MQTT, Modbus and vendor APIs.
Yes, on Factory and Enterprise tiers. Custom models are served through the same Triton-based router and are subject to the same golden-set and promotion gates as ours.
Staged promotion with instant rollback. A new version serves shadow traffic alongside the incumbent until its golden-set performance and twin scenario tests support promotion.
The fastest way to understand the loop is to see it against your own cell and your own inspection data.