The composable approach
Worlds provides check nodes that can be combined to express any detection use case. Which check nodes are available depends on the signal type from your trigger:
Rather than building specialized nodes for each use case, you compose these check types — and optionally add external checks like VLM — to express your business logic.
Track state compositions
Each track check type writes its results to a separate namespace —checks.type1, checks.type2, checks.type3 — and preserves all input data. This means you can chain them in sequence:
checks object and evaluates all results using AND or OR logic to decide whether to create, update, or close an event.
Type I alone — track in zone
The simplest and most common pattern. A single Type I check handles use cases like:- Obstruction/loitering (dwell + velocity thresholds)
- Zone intrusion (intersection threshold)
- Speeding in zone (velocity threshold)
Type I + Type II — zone sequence with thresholds
Combine zone presence conditions with directional/sequence validation:- Wrong-way detection with speed check (was the vehicle going fast in the wrong direction?)
- Checkpoint compliance (did the vehicle stop at checkpoint A before entering zone B?)
Type I + Type III — proximity in zone
Combine zone-based conditions with track-to-track interaction:- Person near forklift in a specific zone
- Two vehicles overlapping in a loading bay
Type III requires batch mode because interaction data is only available after tracks expire.
All three types
For complex safety rules:- Person entered restricted zone (Type I), bypassed the checkpoint (Type II), and was near equipment (Type III)
Zone state checks
For zone state workflows, the Zone Activity Check evaluates zone-level conditions. It writes results tochecks.zoneActivity:
Adding VLM (Vision Language Model) checks
For workflows that capture images, you can add a VLM check as an additional confirmation layer. This uses the built-in LLM nodes rather than a Worlds custom node:{ "confirmed": true, "confidence": 0.92, "context": "..." }). You can use this two ways:
- As a check gate — feed the VLM boolean into an IF node to conditionally create events. This adds AI confirmation to reduce false positives.
- As context metadata — always create the event but attach the VLM description and confidence as event metadata for human review.
Output accumulation
As data flows through multiple check nodes, thechecks object accumulates:
checks entries:
- AND mode — all checks must pass for conditions to be met
- OR mode — any check passing is sufficient

