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: CogVLM, Anthropic Claude, Cache Set, Google Vision OCR, OpenAI, Detections Classes Replacement, Florence-2 Model, Pixelate Visualization, Single-Label Classification Model, SIFT, Moondream2, Stability AI Image Generation, YOLO-World Model, Object Detection Model, Overlap Filter, Cosine Similarity, Triangle Visualization, Model Comparison Visualization, LMM, Segment Anything 2 Model, Keypoint Detection Model, Line Counter, Google Gemini, Byte Tracker, Image Contours, Roboflow Custom Metadata, Circle Visualization, Detections Merge, Trace Visualization, Webhook Sink, Property Definition, LMM For Classification, First Non Empty Or Default, Detections Stitch, SIFT Comparison, JSON Parser, Clip Comparison, Line Counter Visualization, Identify Outliers, Dynamic Zone, Background Color Visualization, Florence-2 Model, Stitch OCR Detections, Image Slicer, Roboflow Dataset Upload, Email Notification, Camera Focus, Environment Secrets Store, Path Deviation, Blur Visualization, Label Visualization, Stability AI Inpainting, Image Preprocessing, Ellipse Visualization, Delta Filter, ONVIF Control, VLM as Classifier, Clip Comparison, Time in Zone, Rate Limiter, Dynamic Crop, Single-Label Classification Model, OpenAI, Keypoint Visualization, Dominant Color, OCR Model, Expression, Keypoint Detection Model, Image Convert Grayscale, Gaze Detection, Detection Offset, Distance Measurement, SmolVLM2, Dimension Collapse, Qwen2.5-VL, OpenAI, VLM as Detector, SIFT Comparison, Mask Visualization, Image Slicer, Barcode Detection, Path Deviation, VLM as Detector, CSV Formatter, Polygon Zone Visualization, Crop Visualization, Classification Label Visualization, Reference Path Visualization, Multi-Label Classification Model, Twilio SMS Notification, Bounding Box Visualization, Size Measurement, Perspective Correction, Slack Notification, Pixel Color Count, Polygon Visualization, Detections Transformation, VLM as Classifier, Instance Segmentation Model, Data Aggregator, Color Visualization, Identify Changes, Detections Consensus, Image Threshold, Absolute Static Crop, Stitch Images, Multi-Label Classification Model, Line Counter, Detections Filter, Dot Visualization, QR Code Detection, Instance Segmentation Model, Model Monitoring Inference Aggregator, Detections Stabilizer, Template Matching, Bounding Rectangle, Roboflow Dataset Upload, Image Blur, Time in Zone, Grid Visualization, CLIP Embedding Model, Object Detection Model, Depth Estimation, Llama 3.2 Vision, Byte Tracker, Continue If, Byte Tracker, Halo Visualization, Corner Visualization, Camera Calibration, Cache Get, Buffer, Relative Static Crop, Velocity, Local File Sink
• 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"
    ]
}