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