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Documentation Index

Fetch the complete documentation index at: https://docs.cyberwave.com/llms.txt

Use this file to discover all available pages before exploring further.

Workflows are Cyberwave’s automation primitive. They turn a graph of nodes — triggers, models, twins, conditions, integrations — into a runnable artifact that can fire on a schedule, an MQTT message, a webhook, or every camera frame on a Raspberry Pi. 
from cyberwave import Cyberwave

cw = Cyberwave()

run = cw.workflows.trigger(
    "acme/workflows/pick-and-place",
    inputs={"target_position": [1.0, 2.0, 0.0], "speed": 0.5},
)
run.wait(timeout=60)
print(run.status)
That same workflow can be opened, edited, and visualised in the Workflow Editor — the graph and the Python are two views of one object.

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Three ways to build one

You never have to pick between a low-code graph and “real” code. Cyberwave keeps the two in sync.

Visual editor

Drag nodes from the palette, wire ports, configure parameters. Great for prototyping and for handing flows to non-engineers.

Python SDK

Build, list, trigger, and monitor workflows from cw.workflows. Anything you can do in the UI you can do in code.

With an agent

Describe the automation in plain English to Claude, Cursor, or any MCP client — Cyberwave’s MCP server and Claude skill wire up the graph for you.
Every workflow round-trips between graph and Python — no lock-in to either format. You get the power of pure code where you need it, and the simplicity of a visual graph everywhere else.

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Cloud, edge, or both — in one graph

A workflow can span both sides of your stack. Trigger and ML nodes that run on the cloud coordinate with the same workflow’s edge half via the edge worker runtime. A common shape: The video frame never leaves the device unanonymized, the VLM runs on a GPU in the cloud, and an email goes out — all from a single workflow definition. The unified edge compiler decides what runs where based on the graph itself. You don’t manage model downloads, hardware compatibility, or reconnects. Cyberwave provisions the right model into the right runtime — edge YOLO for camera_frame, cloud VLMs for call_model — and keeps it healthy when the network blinks.

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Same workflow, sim or real

Workflows run unchanged against simulation or live hardware. Flip the mode on your environment (or the SDK) and the same graph drives a MuJoCo twin in CI, then a real robot in your cell: 
cw.affect("simulation")
cw.workflows.trigger("acme/workflows/pick-and-place")

cw.affect("live")
cw.workflows.trigger("acme/workflows/pick-and-place")
This is the practical version of “test before you deploy” — see the environment editor for replaying past runs and inspecting per-node executions.

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What workflows are good at

Orchestrating multiple twins

Synchronise robots and sensors — swarm formations, multi-arm cells, or a fleet of cameras feeding a single inspection job. Wire any twin from the catalog into the same graph.

Switching between models and code

Hand off mid-task — e.g. an RL policy moves the arm to the box, then a deterministic Python code node takes over for the pre-canned drop sequence.

Reacting to the real world

Camera-frame, MQTT, schedule, webhook, and email triggers wake the workflow up without you wiring anything custom.

Calling out to anything

http_request, send_email, and send_alert nodes plug Cyberwave into the rest of your stack — PagerDuty, Slack, your warehouse system, a Notion page.
Workflows compose the whole Cyberwave ecosystem — hundreds of twins and the models hub are one node away.

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Start from a template

Don’t start from a blank canvas. Cyberwave ships a library of workflow templates — motion detection, pick-and-place, intrusion alerts, voice control — and you can publish your own for your team or the world. 
cyberwave workflow create --template motion-detection
Templates are versioned, slugged like the rest of Cyberwave (acme/workflows/my-template), and editable after cloning — they’re a starting point, not a black box.

Workflows reference

Node taxonomy, trigger types, edge sync, and the full editor walkthrough.

Workflow API

REST schemas: WorkflowSchema, WorkflowExecuteSchema, WorkflowNodeSchema.

Edge-to-cloud VLM tutorial

Build the anonymize-on-edge + VLM-on-cloud workflow end-to-end.