dyon 0.10.2

Domain-agnostic Python framework for digital twin architectures

Dyon

Build domain-agnostic digital twins in Python — from raw sensor data to autonomous decision-making.

Dyon is a domain-agnostic Python framework for building digital twins — live software models of a real-world asset that ingest its sensor data, store and model it, react to it, reason about it, and act on it. The same framework works for a pump, a wind turbine, an HVAC unit, a delivery fleet, or a manufacturing line, because nothing in the core knows anything about your domain. You declare what your asset's sensors look like, write a small amount of asset-specific glue, and the framework supplies the rest.

You switch on only the capabilities your asset needs — a monitoring-only twin can be a few lines, and the same twin can grow into a self-managing one without a rewrite.

Contents

• What you can do
• The dashboard
• Installation
• Quick start
• CLI reference
• Learn more
• License

What you can do

With Dyon you can:

• Ingest live telemetry off an MQTT broker and store it — time-series, documents, cache, and large objects — with an audit trail, no plumbing to write.
• Clean and score incoming readings automatically: smoothing, validation, and a rolling asset-health score.
• Model and forecast the asset — fit a physics model, an ML surrogate, or a time-series forecast, and detect drift against it.
• React without a human in the loop — thresholds, state machines, and closed-loop control that turn readings into actions.
• Diagnose faults in plain language — LLM agents that explain why something is wrong, with a knowledge graph behind them.
• Act autonomously — goal-driven decisions, learned control policies, and escalation to a person when it matters.
• Teach a twin by example — learn a behaviour from demonstrations when it's too hard to write down as a rule or reward.
• Serve and visualise — a REST + streaming API and an opt-in live dashboard (see below).

Each of these is optional and independent: you assemble only what your asset needs, and you never edit the framework to do it.

The dashboard

Dyon ships an opt-in, in-browser dashboard — the human-facing end of a twin. It's built from the config you already wrote: pass one flag and you get a modern app shell whose Home tab foregrounds the asset, a Telemetry tab of KPI tiles, trend charts, an alarm banner and event log, and an Agents tab that shows the multi-agent system working — all updating in real time, with nothing more to author.

app = create_app(config, registry, include_viz=True)   # off by default

It reads the same sensor definitions you already declared, streams live updates to the browser, and wires a chat panel to the twin's diagnostic agent — which can answer with a chart it draws on the fly (mirrored onto a live agent canvas), or with speech. For assets you have a 3D model of, a viewport shows the model and recolours it as the asset's state changes; it checks the viewer's hardware first and falls back to a 2D schematic where the compute isn't there, so the capability is available to those who can run it and never a barrier to those who can't. And when twins combine — a fleet, a composite, a network — one federated dashboard views them together, drilling into each member's own full view. The client needs no front-end build step. Chapter 13 of the guide covers it end to end; dyon dashboard --api <url> serves it against a running twin.

Installation

Dyon requires Python 3.11+. The backing services (MQTT broker, databases, and the optional twin-state and knowledge-graph stores) run in Docker, so you also need Docker with the Compose plugin.

pip install dyon

That installs a lean core — an MQTT→API twin with config, the REST layer, and the client-rendered dashboard — plus the dyon command-line tool. Every heavy capability is an optional extra, so you pull only what a given twin uses:

Extra	Adds
stores	InfluxDB, MongoDB, Redis, MinIO, PostgreSQL adapters
agents	LangChain agents, the Neo4j knowledge graph, sentiment scoring
rl	reinforcement learning + learning-from-demonstration (SB3, gymnasium, imitation)
sim	simulation, reactive control, and surrogate ML
forecast	the Prophet forecasting chart and agent tool
voice	server-side speech (the dashboard otherwise uses the browser's own)
hardware	serial / hardware publishers

Combine them (pip install 'dyon[stores,agents]') or install everything with pip install 'dyon[all]'. A feature whose extra is not installed fails with the exact pip install command that provides it.

Coming from dt-forge?

This framework was previously published as dt-forge (package dt_forge, command dtforge). It is now Dyon, and existing projects keep working with no changes: import dt_forge and from dt_forge.… import … transparently resolve to dyon, and the dtforge command still runs. You will see a one-time DeprecationWarning pointing you at the new name. To migrate, replace dt_forge with dyon in your imports and dtforge with dyon on the command line. The compatibility shim will be removed in a future major release.

Quick start

1. Configure

All configuration comes from environment variables with the DT_ prefix. Nested fields use a double-underscore delimiter — DT_MQTT__BROKER, not DT_MQTT_BROKER (the single-underscore form is silently ignored). Put them in a .env file in your project directory:

DT_ASSET_ID=pump_001
DT_ASSET_TYPE=centrifugal_pump
DT_ASSET_NAME="Plant A Pump"

DT_MQTT__BROKER=localhost
DT_MQTT__PORT=1883

DT_INFLUX__URL=http://localhost:8086
DT_INFLUX__TOKEN=my-super-secret-token
DT_INFLUX__ORG=digital_twin
DT_INFLUX__BUCKET=asset_telemetry

DT_MONGO__URI=mongodb://admin:password@localhost:27017
DT_REDIS__URL=redis://localhost:6379
DT_DITTO__URL=http://localhost:8080

# Optional — only if you use the LLM diagnostic agent
DT_LLM__PROVIDER=anthropic        # openai | anthropic | ollama
DT_LLM__MODEL=claude-sonnet-4-6
DT_LLM__API_KEY=sk-ant-...
DT_NEO4J__URI=bolt://localhost:7687

Every field has a sensible default, so you only set what differs from the defaults.

2. Start the infrastructure you need

dyon infra up writes a docker-compose.yml with exactly the services your twin needs, then runs docker compose up -d:

# Minimal monitoring twin (MQTT broker + the four data stores):
dyon infra up --layers data,network

# Add the twin-state service and the knowledge graph:
dyon infra up --layers data,network,services,intelligent

# Write the compose file without starting anything:
dyon infra up --layers data,network,services --generate-only
docker compose up -d

The command records your selection in a .dyon-layers file. Once the containers are up, verify the twin can reach each one:

dyon infra check          # reads .dyon-layers automatically

Every reachable service prints a ✓.

3. Scaffold a twin

dyon init \
  --asset-type centrifugal_pump \
  --name "Plant A Pump" \
  --asset-id pump_001

That writes two files into the output directory:

File	Purpose
twin.py	A runnable twin — fill in your sensor fields and run it
.env	Environment configuration, pre-filled with the defaults

The generated twin.py runs as soon as you fill in your sensor fields, ingesting telemetry, storing it, scoring health, and raising warning/critical events automatically. Add modelling, diagnosis, and autonomy as your twin grows.

4. Run

python twin.py            # run the module directly
# or, from the project directory:
dyon run twin          # imports twin.py and drives its lifecycle

If you enabled the API, the FastAPI app serves these endpoints:

Endpoint	Returns
GET /health	Liveness — {asset_id, status}
GET /api/twin/state	Current twin state
GET /api/twin/telemetry	Latest telemetry
GET /api/twin/health-score	Current health score
GET /api/twin/events	Recent events
POST /api/chat	Streamed replies from the LLM diagnostic agent

Point a real device (or the bundled simulator) at the twin's telemetry topic and it starts working immediately.

CLI reference

dyon init        --asset-type TYPE --name NAME --asset-id ID [--out DIR]
dyon infra up    --layers ... [--out FILE] [--generate-only]
dyon infra check [--layers ...]              # default: read .dyon-layers
dyon run         [TWIN_MODULE]               # default module: "twin"
dyon train       [--algorithm SAC|TD3|PPO|A2C] [--timesteps N]
                    [--env-module M] [--save NAME]
dyon dashboard   [--api URL] [--port N] [--open/--no-open]   # serve the dashboard

Run any command with --help for the full flag list.

Security model

Out of the box Dyon runs in dev mode: default credentials, open CORS, no API key, and the API bound to 127.0.0.1. That keeps a local twin zero-config, and is safe on a trusted machine or lab network — but only there.

For anything exposed, switch to production mode with DT_SECURITY__MODE=production. The twin then refuses to start until every factory-default credential is replaced, an API key is set (DT_SECURITY__API_KEY — required on all /api/* HTTP and WebSocket routes), and CORS is restricted to the origins you list. Enable MQTT TLS for a remote broker, and terminate HTTPS at a reverse proxy in front of the API. The full checklist is in SECURITY.md.

Learn more

The guide/ folder in the source repository is the complete developer manual, taking you from your first twin to a full worked example and a live dashboard.

License

Dyon is dual-licensed.

• Noncommercial use is free under the PolyForm Noncommercial License 1.0.0. This covers personal projects, academic research, teaching, evaluation, and use by nonprofit and government organisations. The full terms — including what counts as a permitted purpose — are in the LICENSE file.
• Commercial use requires a separate commercial license. Any use in or for the benefit of a for-profit business, or any use with an anticipated commercial application, needs a paid license. To arrange one, contact [e-mail].

PolyForm Noncommercial is a source-available license, not an OSI-approved open-source license: the source is published and free to read, modify, and use for noncommercial purposes, but commercial rights are reserved.