Worlds Actions

The Worlds Actions node is a multi-resource node that provides access to various Worlds platform capabilities beyond event management. Select a resource to access different operations.

Resources

Process Detection Image

Create still images or animated GIFs from detection data. Used to capture visual evidence for events.

Create Still Image
Create GIF

Generates a single annotated image showing the detection at a specific timestamp.

Parameter	Type	Description
Track IDs	Array	Track IDs to include in the image
Timestamp	DateTime	The point in time to capture
Zone IDs	Array	Zones to overlay on the image

Returns a processedImage field containing the base64-encoded image and a metadata object with the timestamp.

Generates an animated GIF showing the detection sequence over time.

Parameter	Type	Description
Track IDs	Array	Track IDs to include
Timestamp	DateTime	Reference timestamp

Returns a gif_base64 field containing the base64-encoded GIF.

Send Worlds Email

Send email notifications using the Worlds SendGrid email template system.

Parameter	Type	Description
Email Subject	String	Subject line
Alert Image	String	Base64-encoded image or GIF to include
Alert Title	String	Title displayed in the email
Site Name	String	Site name for context
Event Timestamp	DateTime	When the event occurred
Camera Name	String	Camera/data source name
Event ID	String	Worlds event ID for linking
To Recipients	Collection	Email addresses to send to
CC/BCC Recipients	Collection	Additional recipients (in advanced options)

Credentials: Requires SendGrid API credentials.

AI Operations

Access Worlds AI capabilities:

Operation	Description
OCR	Extract text from detection images
Image Segmentation	Segment objects in detection images
Embeddings	Generate vector embeddings from detections

Get Track State

Query the current state of a track from the state machine.

Get Zone State

Query the current state of a zone from the state machine.

Create Event

Create events directly (alternative to Event Manager for simpler workflows).

Event Producer

Query available event producers for your organization.

Get Event

Query existing events from the Worlds platform.

Get Track Snapshot

Compute a track’s position, velocity, and zone membership at a specific historical timestamp. Unlike Get Track State which returns the latest or final state, this resource analyzes raw detections within a time window around the requested timestamp to reconstruct what the track was doing at that moment. This is particularly useful in batch workflows after a Type III check, where you need to know what a track was doing at the moment of an interaction — for example, its velocity when two tracks were closest, or which zone it occupied at the time of a near-miss.

Parameter	Type	Default	Description
Track IDs	Array	—	One or more track UUIDs to query
Timestamps	Array	—	ISO 8601 timestamp per track, or a single shared timestamp for all tracks
Window Seconds	Number	10	Seconds before and after the timestamp to include in computation

Output:

{
  "track_snapshots": {
    "019bb7ea-4050-7e9b-8761-f8df773fd3e5": {
      "track_id": "019bb7ea-4050-7e9b-8761-f8df773fd3e5",
      "tag": "Person",
      "timestamp": "2026-01-13T15:12:52.000Z",
      "window_seconds": 10,
      "detections_in_window": 14,
      "position": {
        "method": "linear",
        "offset_ms": 82,
        "pix": { "x": 512, "y": 347 },
        "geo": { "lon": -73.9857, "lat": 40.7484 },
        "polygon": [[480, 290], [544, 290], [544, 404], [480, 404]]
      },
      "motion": {
        "pix": {
          "avg_velocity": 22.4,
          "max_velocity": 38.1,
          "direction": 135.0,
          "sample_count": 13
        },
        "geo": {
          "avg_velocity": 1.1,
          "max_velocity": 1.9,
          "direction": 135.0,
          "sample_count": 13
        }
      },
      "zones": {
        "active_at_timestamp": {
          "a3f1c2d4-8899-4b6e-b012-567890abcdef": {
            "zone_id": "a3f1c2d4-8899-4b6e-b012-567890abcdef",
            "zone_name": "Loading Dock",
            "intersection_percent": 0.78,
            "dwell_at_timestamp": 14.5
          }
        }
      }
    }
  }
}

The method field tells you how the position was determined:

real — an exact detection exists at the requested timestamp
linear — interpolated between two bracketing detections
forward_extrapolation / backward_extrapolation — the timestamp is outside the detection range, so the nearest detection was used

Credentials: Requires GraphQL Subscription API credentials when querying tracks not already cached by the state machine.

Closest Frame

Find the optimal timestamp where multiple tracks are closest together in a single camera frame. This is particularly useful in zone state workflows where you’re working with multiple tracks and need a single image that shows them all.

Parameter	Type	Description
Track IDs	Array	Track IDs to analyze
Timestamp	DateTime	Reference timestamp to search around

The node calculates the minimax edge-to-edge bounding box distance across all specified tracks and returns the timestamp where they are closest together. Output:

{
  "closest_frame": {
    "optimal_timestamp": "2026-02-19T18:36:22.045Z"
  }
}

Use the optimal_timestamp as the timestamp input for the Process Detection Image node to capture the best possible image of all tracks together.

Closest Frame serves a similar purpose to batch mode’s interaction data, but works with streaming zone state data. In batch workflows where interactions are available, you may not need this node. In zone state workflows, it’s the recommended way to find the best image timestamp for multi-track scenarios.

Using VLM (Vision Language Model) with images

A common advanced pattern is to pass a captured still image through a Vision Language Model for additional analysis. This uses the built-in Basic LLM Chain node with image input:

Capture a still image using Process Detection Image
Pass the image binary to a VLM (Azure OpenAI, GPT-4V, etc.) with a structured prompt
Use a Structured Output Parser to get typed JSON output (e.g., { "boolean": true, "confidence": 0.92, "context": "..." })
Merge the VLM output back with the original data

The VLM output can be used two ways:

As context metadata — attach the VLM’s description and confidence to the event for human review
As a check gate — use the boolean output with an IF node to conditionally create events, reducing false positives

See the Streaming Zone State Workflow for a complete example using VLM confirmation.

Credentials

Most resources require GraphQL Subscription API credentials. The email resource requires SendGrid API credentials.

Trigger Nodes

Check Nodes

Event Nodes

Action Nodes

Resources

Process Detection Image

Send Worlds Email

AI Operations

Get Track State

Get Zone State

Create Event

Event Producer

Get Event

Get Track Snapshot

Closest Frame

Using VLM (Vision Language Model) with images

Credentials

​Resources

​Process Detection Image

​Send Worlds Email

​AI Operations

​Get Track State

​Get Zone State

​Create Event

​Event Producer

​Get Event

​Get Track Snapshot

​Closest Frame

​Using VLM (Vision Language Model) with images

​Credentials

Resources

Process Detection Image

Send Worlds Email

AI Operations

Get Track State

Get Zone State

Create Event

Event Producer

Get Event

Get Track Snapshot

Closest Frame

Using VLM (Vision Language Model) with images

Credentials