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