Cache Get¶
Class: CacheGetBlockV1
Source: inference.core.workflows.core_steps.cache.cache_get.v1.CacheGetBlockV1
Retrieve a previously stored value from an in-memory cache by key, using the image's video identifier as a namespace to enable data sharing between workflow steps, caching intermediate results, and avoiding redundant computations within the same workflow execution context.
How This Block Works¶
This block retrieves values from an in-memory cache that was previously stored using the Cache Set block. The block:
- Receives image and cache key:
- Takes an input image to determine the cache namespace
- Receives a cache key (string) identifying which value to retrieve
- Determines cache namespace:
- Extracts video identifier from the image's video metadata
- Uses the video identifier as the cache namespace (isolates cache entries per video/stream)
- Falls back to "default" namespace if no video identifier is present
- Looks up cached value:
- Accesses the in-memory cache dictionary for the determined namespace
- Searches for the specified key in the cache
- Returns the cached value if found, or False if the key does not exist
- Returns retrieved value:
- Outputs the cached value (can be any data type: strings, numbers, lists, detections, etc.)
- Returns False if the key was not found in the cache
- The output type matches whatever was originally stored with Cache Set
The cache is namespaced by video identifier, meaning different videos or streams have separate cache storage. This allows workflows processing multiple videos to maintain separate caches for each video. The cache is stored in memory and is cleared when the workflow execution completes or when the block is destroyed. Cache Get must be used in conjunction with Cache Set - values are stored with Cache Set and retrieved with Cache Get using the same key and namespace (determined by the same video identifier).
Common Use Cases¶
- Shared State Between Steps: Store intermediate results in one workflow step and retrieve them in another step (e.g., store detection results for later analysis, cache classification predictions for filtering, share metadata between blocks), enabling state sharing workflows
- Avoid Redundant Computations: Cache expensive computation results and reuse them across multiple workflow steps (e.g., cache model predictions, store processed images, reuse transformation results), enabling computation caching workflows
- Video Frame Context: Maintain context across video frames by storing frame-specific data (e.g., cache previous frame detections, store frame sequence metadata, maintain tracking state), enabling frame context workflows
- Conditional Workflow Logic: Store decision results or flags that control workflow execution in subsequent steps (e.g., cache filtering decisions, store validation results, maintain workflow state), enabling conditional execution workflows
- Data Aggregation: Accumulate data across workflow steps by storing values in cache and retrieving/updating them (e.g., aggregate detection counts, accumulate statistics, build result collections), enabling data aggregation workflows
- Temporary Storage: Use cache as temporary storage for values that need to be accessed by multiple workflow steps without passing through the workflow graph (e.g., store cross-step data, maintain temporary state, share non-linear workflow data), enabling temporary storage workflows
Connecting to Other Blocks¶
This block retrieves cached values and can be used throughout workflows:
- After Cache Set block to retrieve values that were previously stored (e.g., retrieve stored detections, get cached predictions, access stored metadata), enabling cache retrieval workflows
- In workflow branches to access shared cache values from parallel or conditional execution paths (e.g., retrieve shared state, access cached results, get common data), enabling branch coordination workflows
- Before blocks that need cached data to provide cached values as input (e.g., provide cached detections to analysis, use cached predictions for filtering, pass cached metadata to processing), enabling cached input workflows
- In conditional logic workflows to retrieve flags or decisions stored by Cache Set (e.g., get cached validation results, retrieve decision flags, access conditional state), enabling conditional logic workflows
- With video processing workflows to maintain frame-specific or video-specific cache namespaces (e.g., retrieve frame context, access video-specific cache, get stream-specific data), enabling video context workflows
- Before output or sink blocks to include cached data in final results (e.g., include cached aggregations, output cached statistics, return cached results), enabling output workflows
Requirements¶
This block requires an input image (used to determine the cache namespace via video identifier) and a cache key (string) to look up the stored value. The block only works in LOCAL execution mode - it will raise a NotImplementedError if used in other execution modes. Values must be previously stored using the Cache Set block with the same key and namespace (same video identifier). The cache is stored in memory and is automatically cleared when the workflow execution completes. The cache is namespaced by video identifier, so different videos have separate cache storage. If a key is not found in the cache, the block returns False. The cached value can be any data type (strings, numbers, lists, detections, images, etc.) depending on what was originally stored.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/cache_get@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
key |
str |
Cache key (string) identifying which value to retrieve from the cache. The key must match the key used when storing the value with the Cache Set block. If the key does not exist in the cache, the block returns False. Keys are case-sensitive and must be exact matches. Use descriptive keys to identify different cached values (e.g., 'detections', 'classification_result', 'frame_metadata').. | ✅ |
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 Cache Get in version v1.
- inputs:
Email Notification,Instance Segmentation Model,Google Vision OCR,S3 Sink,Roboflow Dataset Upload,Anthropic Claude,Twilio SMS/MMS Notification,Keypoint Detection Model,Model Monitoring Inference Aggregator,Florence-2 Model,GLM-OCR,OCR Model,VLM As Classifier,Multi-Label Classification Model,Google Gemini,LMM,Slack Notification,OpenAI,OpenAI,Local File Sink,Twilio SMS Notification,Email Notification,Qwen3.5-VL,Stitch OCR Detections,Webhook Sink,Florence-2 Model,Anthropic Claude,Stitch OCR Detections,LMM For Classification,Google Gemini,EasyOCR,Roboflow Dataset Upload,Llama 3.2 Vision,CSV Formatter,OpenAI,Clip Comparison,Anthropic Claude,VLM As Detector,Google Gemini,Roboflow Custom Metadata,CogVLM,Object Detection Model,OpenAI,Single-Label Classification Model - outputs:
Detections Classes Replacement,Dominant Color,Instance Segmentation Model,ByteTrack Tracker,PTZ Tracking (ONVIF),Contrast Equalization,Google Vision OCR,Grid Visualization,Identify Changes,Trace Visualization,SIFT,Twilio SMS/MMS Notification,QR Code Generator,SAM 3,GLM-OCR,Delta Filter,SORT Tracker,Perception Encoder Embedding Model,OpenAI,OpenAI,Identify Outliers,Pixelate Visualization,Twilio SMS Notification,Label Visualization,Email Notification,Corner Visualization,Background Subtraction,Mask Area Measurement,Time in Zone,Qwen2.5-VL,Morphological Transformation,SAM 3,Path Deviation,Line Counter,OpenAI,Clip Comparison,Background Color Visualization,Camera Calibration,Stability AI Outpainting,Ellipse Visualization,Detections Stitch,Dynamic Zone,VLM As Classifier,Heatmap Visualization,Velocity,Image Convert Grayscale,Detections Consensus,Property Definition,Detections Filter,Size Measurement,Cache Get,Barcode Detection,Color Visualization,Camera Focus,Rate Limiter,Image Slicer,SmolVLM2,SAM 3,Detections Combine,Polygon Visualization,Motion Detection,Blur Visualization,Qwen3-VL,LMM,Bounding Rectangle,Slack Notification,Stability AI Inpainting,Detection Event Log,Instance Segmentation Model,Florence-2 Model,Perspective Correction,Anthropic Claude,Gaze Detection,Mask Visualization,Moondream2,QR Code Detection,Image Contours,Clip Comparison,Byte Tracker,YOLO-World Model,JSON Parser,Keypoint Detection Model,Object Detection Model,VLM As Detector,Pixel Color Count,Path Deviation,Dynamic Crop,Line Counter,Relative Static Crop,Byte Tracker,Email Notification,S3 Sink,Stability AI Image Generation,Model Comparison Visualization,Absolute Static Crop,Roboflow Dataset Upload,Keypoint Visualization,CLIP Embedding Model,Buffer,First Non Empty Or Default,Model Monitoring Inference Aggregator,Reference Path Visualization,Cache Set,Halo Visualization,OCR Model,SIFT Comparison,VLM As Classifier,Time in Zone,Detections Stabilizer,Expression,VLM As Detector,Image Preprocessing,Crop Visualization,Classification Label Visualization,Local File Sink,Qwen3.5-VL,Stitch OCR Detections,Stitch Images,Time in Zone,Stitch OCR Detections,LMM For Classification,EasyOCR,CSV Formatter,Single-Label Classification Model,Anthropic Claude,Image Threshold,Google Gemini,Halo Visualization,Roboflow Custom Metadata,CogVLM,OpenAI,Single-Label Classification Model,Triangle Visualization,Semantic Segmentation Model,Image Blur,Depth Estimation,Detections Transformation,Dimension Collapse,SIFT Comparison,Overlap Filter,Text Display,Data Aggregator,Anthropic Claude,Dot Visualization,Template Matching,Keypoint Detection Model,Florence-2 Model,Circle Visualization,Cosine Similarity,Multi-Label Classification Model,Google Gemini,Detections Merge,Icon Visualization,Detections List Roll-Up,Camera Focus,Detection Offset,Polygon Visualization,Webhook Sink,Polygon Zone Visualization,Google Gemini,Roboflow Dataset Upload,Distance Measurement,Llama 3.2 Vision,Seg Preview,Object Detection Model,Image Slicer,Byte Tracker,Line Counter Visualization,Continue If,Multi-Label Classification Model,OC-SORT Tracker,Bounding Box Visualization,Segment Anything 2 Model
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Cache Get in version v1 has.
Bindings
-
input
image(image): Input image used to determine the cache namespace. The block extracts the video identifier from the image's video metadata and uses it as the cache namespace. If no video identifier is present, the block uses 'default' as the namespace. The namespace isolates cache entries so different videos or streams have separate cache storage. Use the same image (with the same video identifier) for both Cache Set and Cache Get blocks to access the same cache namespace..key(string): Cache key (string) identifying which value to retrieve from the cache. The key must match the key used when storing the value with the Cache Set block. If the key does not exist in the cache, the block returns False. Keys are case-sensitive and must be exact matches. Use descriptive keys to identify different cached values (e.g., 'detections', 'classification_result', 'frame_metadata')..
-
output
output(*): Equivalent of any element.
Example JSON definition of step Cache Get in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/cache_get@v1",
"image": "$inputs.image",
"key": "my_cache_key"
}