Continue If

Class: ContinueIfBlockV1

Source: inference.core.workflows.core_steps.flow_control.continue_if.v1.ContinueIfBlockV1

Based on provided configuration, block decides if it should follow to pointed execution path

Type identifier

Use the following identifier in step "type" field: roboflow_core/continue_if@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
condition_statement	StatementGroup	Define the conditional logic..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Continue If in version v1.

• inputs: Camera Calibration, Stitch OCR Detections, CSV Formatter, Instance Segmentation Model, Cosine Similarity, CogVLM, Absolute Static Crop, Distance Measurement, Object Detection Model, Florence-2 Model, Byte Tracker, Label Visualization, Path Deviation, Single-Label Classification Model, Dimension Collapse, Detections Stitch, Model Comparison Visualization, Slack Notification, Object Detection Model, VLM as Classifier, Bounding Rectangle, Detections Consensus, Data Aggregator, Detection Offset, Halo Visualization, Overlap Filter, Keypoint Detection Model, Time in Zone, First Non Empty Or Default, Reference Path Visualization, Perspective Correction, Dynamic Crop, Detections Transformation, Camera Focus, Delta Filter, Detections Classes Replacement, VLM as Detector, Cache Set, Local File Sink, Path Deviation, OpenAI, Google Vision OCR, VLM as Detector, Stability AI Image Generation, Ellipse Visualization, Blur Visualization, Circle Visualization, Image Blur, Background Color Visualization, Multi-Label Classification Model, Single-Label Classification Model, Rate Limiter, Detections Filter, Pixelate Visualization, Identify Outliers, Google Gemini, Stability AI Inpainting, Keypoint Detection Model, Keypoint Visualization, Template Matching, Mask Visualization, SmolVLM2, YOLO-World Model, Trace Visualization, JSON Parser, Polygon Visualization, Segment Anything 2 Model, Expression, LMM, Depth Estimation, Classification Label Visualization, LMM For Classification, Clip Comparison, Line Counter, Stitch Images, Byte Tracker, Image Slicer, Roboflow Dataset Upload, Multi-Label Classification Model, Twilio SMS Notification, SIFT Comparison, OpenAI, QR Code Detection, Environment Secrets Store, Qwen2.5-VL, Roboflow Dataset Upload, Bounding Box Visualization, Llama 3.2 Vision, Pixel Color Count, Moondream2, Grid Visualization, Detections Merge, Continue If, Dynamic Zone, Image Convert Grayscale, Buffer, Triangle Visualization, Model Monitoring Inference Aggregator, Time in Zone, Cache Get, Identify Changes, Florence-2 Model, Size Measurement, SIFT, Dot Visualization, CLIP Embedding Model, Gaze Detection, Color Visualization, Byte Tracker, Dominant Color, Clip Comparison, VLM as Classifier, Polygon Zone Visualization, Relative Static Crop, Property Definition, Detections Stabilizer, OCR Model, Velocity, Image Preprocessing, Line Counter Visualization, Roboflow Custom Metadata, Anthropic Claude, Webhook Sink, SIFT Comparison, Instance Segmentation Model, Corner Visualization, Barcode Detection, Crop Visualization, Email Notification, Image Contours, Image Slicer, Line Counter, Image Threshold
• outputs: None

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Continue If in version v1 has.

Bindings

• input

  • evaluation_parameters (*): Data to be used in the conditional logic..
  • next_steps (step): Steps to execute if the condition evaluates to true..

• output

Example JSON definition of step Continue If in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/continue_if@v1",
    "condition_statement": {
        "statements": [
            {
                "comparator": {
                    "type": "(Number) =="
                },
                "left_operand": {
                    "operand_name": "left",
                    "type": "DynamicOperand"
                },
                "right_operand": {
                    "type": "StaticOperand",
                    "value": 1
                },
                "type": "BinaryStatement"
            }
        ],
        "type": "StatementGroup"
    },
    "evaluation_parameters": {
        "left": "$inputs.some"
    },
    "next_steps": [
        "$steps.on_true"
    ]
}