Roboflow Asset Library Attributes¶
Class: RoboflowAssetLibraryAttributesBlockV1
Submit attribute and tag updates for existing Asset Library images, enabling enrichment workflows where model outputs become filterable image fields.
How This Block Works¶
This block submits key-value attributes and tags for existing Asset Library images in your Roboflow workspace. Attribute values are stored as image metadata. The block:
- Receives Asset Library source image IDs, optional attributes, and optional tags
- Resolves the target workspace from the configured Roboflow API key
- Skips rows where both attributes and tags are empty
- Merges duplicate source IDs using sequential semantics: later attribute values win, and tags are added as a de-duplicated set
- Submits one batch update request and returns one submission status per input source ID, in input order
Re-running the same workflow against the same source IDs is safe: attribute keys are upserted (last write wins) and tags are unioned. There is no destructive write.
The block does not send image bytes and does not create new images. It only updates existing Asset Library source images. Removing attribute keys, removing tags, writing annotations, and creating images are intentionally out of scope for this workflow block.
Requirements¶
This block requires a valid Roboflow API key. The API key determines the workspace whose Asset Library images can be updated.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/asset_library_attributes@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
source_id |
str |
Asset Library source image ID to update. For batch workflows, provide one source ID per image.. | ✅ |
metadata |
Dict[str, Union[bool, float, int, str]] |
Optional key-value attributes to set on the Asset Library image. Attributes are stored as image metadata. Either an inline dict whose values may be static or selector references (e.g. $inputs.camera_id), or a whole-field selector to a per-row dict produced by an upstream step.. |
✅ |
tags |
List[str] |
Optional tags to add to the Asset Library image. Each entry may be a static string or a reference to a workflow input/step (e.g. $inputs.label).. |
✅ |
disable_sink |
bool |
If True, the block execution is disabled and no Asset Library attribute writes occur.. | ✅ |
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 Roboflow Asset Library Attributes in version v1.
- inputs:
Detections Stabilizer,Velocity,Keypoint Detection Model,Anthropic Claude,Instance Segmentation Model,SIFT Comparison,Google Vision OCR,Circle Visualization,Detections Filter,Google Gemini,Detections Merge,Byte Tracker,Roboflow Vision Events,Line Counter Visualization,VLM As Detector,Morphological Transformation,LMM,Model Comparison Visualization,Buffer,MoonshotAI Kimi,Segment Anything 2 Model,Cache Set,Instance Segmentation Model,Twilio SMS/MMS Notification,Detections Classes Replacement,Twilio SMS Notification,SAM 3,S3 Sink,Local File Sink,Halo Visualization,Camera Focus,SIFT Comparison,Semantic Segmentation Model,Mask Visualization,Path Deviation,MoonshotAI Kimi,Text Display,Image Slicer,Absolute Static Crop,Inner Workflow,VLM As Classifier,Path Deviation,GLM-OCR,Roboflow Custom Metadata,Detections Combine,Dynamic Crop,Mask Area Measurement,Cosine Similarity,Model Monitoring Inference Aggregator,Contrast Enhancement,Motion Detection,Webhook Sink,SAM2 Video Tracker,Color Visualization,Object Detection Model,YOLO-World Model,Google Gemini,Single-Label Classification Model,Anthropic Claude,QR Code Generator,Clip Comparison,Environment Secrets Store,Qwen3.5,Bounding Box Visualization,Florence-2 Model,VLM As Classifier,Overlap Filter,Image Threshold,Cache Get,Line Counter,Relative Static Crop,Qwen3.5-VL,Moondream2,Perception Encoder Embedding Model,Llama 3.2 Vision,Time in Zone,Image Convert Grayscale,Byte Tracker,Keypoint Visualization,Google Gemma,Image Stack,Morphological Transformation,Email Notification,Rate Limiter,Corner Visualization,Stitch OCR Detections,Detections Transformation,OpenAI,Background Color Visualization,JSON Parser,Single-Label Classification Model,Identify Changes,Ellipse Visualization,Stability AI Outpainting,Data Aggregator,EasyOCR,OCR Model,Perspective Correction,Stitch OCR Detections,Instance Segmentation Model,Time in Zone,Background Subtraction,Template Matching,Pixel Color Count,Stability AI Inpainting,Distance Measurement,Barcode Detection,Image Slicer,Mask Edge Snap,Identify Outliers,Image Contours,Qwen 3.6 API,Single-Label Classification Model,CLIP Embedding Model,Depth Estimation,Stitch Images,Grid Visualization,OpenAI,Clip Comparison,Dominant Color,Continue If,Qwen-VL,SAM 3,Keypoint Detection Model,Multi-Label Classification Model,SmolVLM2,SIFT,Anthropic Claude,Roboflow Dataset Upload,Multi-Label Classification Model,Llama 3.2 Vision,PTZ Tracking (ONVIF),Object Detection Model,Email Notification,Seg Preview,Instance Segmentation Model,Overlap Analysis,Time in Zone,OpenRouter,Qwen3-VL,Per-Class Confidence Filter,Google Gemma API,Stability AI Image Generation,Heatmap Visualization,Contrast Equalization,Property Definition,Roboflow Dataset Upload,Slack Notification,Detection Offset,OpenAI,SAM 3,Expression,Image Blur,Keypoint Detection Model,Detections Consensus,Qwen2.5-VL,Dot Visualization,Polygon Zone Visualization,Label Visualization,Icon Visualization,LMM For Classification,Object Detection Model,OC-SORT Tracker,Blur Visualization,Delta Filter,Bounding Rectangle,Dimension Collapse,Trace Visualization,Size Measurement,Dynamic Zone,Florence-2 Model,Camera Focus,QR Code Detection,CogVLM,Pixelate Visualization,Polygon Visualization,Line Counter,Classification Label Visualization,Multi-Label Classification Model,Gaze Detection,Camera Calibration,Google Gemini,Image Preprocessing,Semantic Segmentation Model,Halo Visualization,Byte Tracker,Roboflow Asset Library Attributes,ByteTrack Tracker,Reference Path Visualization,Detections Stitch,CSV Formatter,VLM As Detector,Triangle Visualization,Crop Visualization,Qwen 3.5 API,Detection Event Log,OpenAI-Compatible LLM,First Non Empty Or Default,Detections List Roll-Up,BoT-SORT Tracker,SORT Tracker,Polygon Visualization,OpenAI - outputs:
Keypoint Detection Model,Distance Measurement,Instance Segmentation Model,Anthropic Claude,Google Vision OCR,Circle Visualization,Google Gemini,Qwen 3.6 API,Single-Label Classification Model,CLIP Embedding Model,Roboflow Vision Events,Depth Estimation,Line Counter Visualization,Morphological Transformation,LMM,Model Comparison Visualization,Segment Anything 2 Model,MoonshotAI Kimi,Cache Set,Instance Segmentation Model,Twilio SMS/MMS Notification,OpenAI,Detections Classes Replacement,Twilio SMS Notification,SAM 3,Qwen-VL,S3 Sink,SAM 3,Halo Visualization,SIFT Comparison,Keypoint Detection Model,Local File Sink,Multi-Label Classification Model,Semantic Segmentation Model,Mask Visualization,Path Deviation,Anthropic Claude,MoonshotAI Kimi,Roboflow Dataset Upload,Text Display,Multi-Label Classification Model,Llama 3.2 Vision,PTZ Tracking (ONVIF),Path Deviation,GLM-OCR,Object Detection Model,Email Notification,Roboflow Custom Metadata,Seg Preview,Dynamic Crop,Instance Segmentation Model,Time in Zone,OpenRouter,Model Monitoring Inference Aggregator,Motion Detection,Webhook Sink,Google Gemma API,Stability AI Image Generation,Heatmap Visualization,Color Visualization,Contrast Equalization,Object Detection Model,YOLO-World Model,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Slack Notification,Anthropic Claude,QR Code Generator,Clip Comparison,OpenAI,Bounding Box Visualization,SAM 3,Florence-2 Model,Image Blur,Keypoint Detection Model,Detections Consensus,Dot Visualization,Polygon Zone Visualization,Label Visualization,Icon Visualization,Image Threshold,Cache Get,LMM For Classification,Object Detection Model,Blur Visualization,Line Counter,Trace Visualization,Moondream2,Size Measurement,Dynamic Zone,Perception Encoder Embedding Model,Florence-2 Model,CogVLM,Pixelate Visualization,Time in Zone,Llama 3.2 Vision,Keypoint Visualization,Polygon Visualization,Line Counter,Google Gemma,Classification Label Visualization,Multi-Label Classification Model,Image Stack,Gaze Detection,Morphological Transformation,Camera Calibration,Email Notification,Google Gemini,Image Preprocessing,Corner Visualization,Stitch OCR Detections,Halo Visualization,Roboflow Asset Library Attributes,Reference Path Visualization,OpenAI,Background Color Visualization,Detections Stitch,Single-Label Classification Model,Ellipse Visualization,Stability AI Outpainting,Triangle Visualization,Crop Visualization,Perspective Correction,Qwen 3.5 API,Stitch OCR Detections,OpenAI-Compatible LLM,BoT-SORT Tracker,Instance Segmentation Model,Time in Zone,Polygon Visualization,Template Matching,Pixel Color Count,OpenAI,Stability AI Inpainting
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Roboflow Asset Library Attributes in version v1 has.
Bindings
-
input
source_id(string): Asset Library source image ID to update. For batch workflows, provide one source ID per image..metadata(Union[*,dictionary]): Optional key-value attributes to set on the Asset Library image. Attributes are stored as image metadata. Either an inline dict whose values may be static or selector references (e.g.$inputs.camera_id), or a whole-field selector to a per-row dict produced by an upstream step..tags(Union[list_of_values,string]): Optional tags to add to the Asset Library image. Each entry may be a static string or a reference to a workflow input/step (e.g.$inputs.label)..disable_sink(boolean): If True, the block execution is disabled and no Asset Library attribute writes occur..
-
output
Example JSON definition of step Roboflow Asset Library Attributes in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/asset_library_attributes@v1",
"source_id": "$inputs.source_id",
"metadata": {
"color": "red",
"score": 0.98
},
"tags": [
"auto-labeled",
"red"
],
"disable_sink": false
}