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:
OCR Model,Email Notification,Webhook Sink,OpenAI,Google Vision OCR,Google Gemini,Instance Segmentation Model,Local File Sink,Single-Label Classification Model,Model Monitoring Inference Aggregator,Anthropic Claude,Multi-Label Classification Model,Florence-2 Model,Keypoint Detection Model,Llama 3.2 Vision,Email Notification,Slack Notification,Twilio SMS/MMS Notification,Roboflow Custom Metadata,Florence-2 Model,LMM For Classification,Roboflow Dataset Upload,CSV Formatter,Stitch OCR Detections,OpenAI,Qwen3.5-VL,Object Detection Model,Anthropic Claude,Google Gemini,Google Gemini,VLM As Detector,EasyOCR,LMM,CogVLM,S3 Sink,Anthropic Claude,VLM As Classifier,Twilio SMS Notification,OpenAI,Stitch OCR Detections,Roboflow Dataset Upload,OpenAI,Clip Comparison - outputs:
Dynamic Crop,OCR Model,Barcode Detection,Image Blur,Background Subtraction,Google Vision OCR,Google Gemini,Image Preprocessing,Object Detection Model,Local File Sink,Single-Label Classification Model,Multi-Label Classification Model,Bounding Box Visualization,Model Monitoring Inference Aggregator,Keypoint Detection Model,Identify Outliers,Camera Focus,Gaze Detection,Florence-2 Model,Dot Visualization,Roboflow Dataset Upload,CSV Formatter,Depth Estimation,Polygon Visualization,OpenAI,Line Counter,Detections List Roll-Up,Image Slicer,Line Counter Visualization,Heatmap Visualization,Morphological Transformation,Distance Measurement,Stability AI Image Generation,Rate Limiter,Google Gemini,Keypoint Visualization,Keypoint Detection Model,Background Color Visualization,QR Code Detection,Label Visualization,Polygon Visualization,LMM,CogVLM,Single-Label Classification Model,Time in Zone,Triangle Visualization,Stability AI Outpainting,Mask Visualization,Color Visualization,Detections Combine,Dominant Color,Text Display,Bounding Rectangle,Reference Path Visualization,Llama 3.2 Vision,OpenAI,Image Threshold,Clip Comparison,Classification Label Visualization,Clip Comparison,Polygon Zone Visualization,Delta Filter,Image Contours,VLM As Classifier,Continue If,Roboflow Custom Metadata,Dynamic Zone,LMM For Classification,Velocity,Halo Visualization,Semantic Segmentation Model,Blur Visualization,Path Deviation,Absolute Static Crop,Anthropic Claude,SAM 3,Cosine Similarity,Detections Transformation,Ellipse Visualization,Identify Changes,Crop Visualization,SIFT Comparison,Path Deviation,Trace Visualization,Twilio SMS Notification,Size Measurement,Stitch Images,Detections Stabilizer,Detections Merge,Time in Zone,Motion Detection,Email Notification,OpenAI,SIFT Comparison,Qwen2.5-VL,Seg Preview,Time in Zone,Instance Segmentation Model,Multi-Label Classification Model,Anthropic Claude,Twilio SMS/MMS Notification,Slack Notification,Detections Stitch,Email Notification,VLM As Detector,Camera Focus,Cache Set,Stitch OCR Detections,SAM 3,Moondream2,PTZ Tracking (ONVIF),Perspective Correction,Expression,Camera Calibration,Icon Visualization,Corner Visualization,Qwen3.5-VL,Overlap Filter,Byte Tracker,VLM As Detector,Halo Visualization,Detection Event Log,JSON Parser,Pixelate Visualization,Qwen3-VL,Contrast Equalization,Dimension Collapse,VLM As Classifier,Data Aggregator,Instance Segmentation Model,Detections Classes Replacement,Relative Static Crop,Line Counter,Stitch OCR Detections,Webhook Sink,Circle Visualization,First Non Empty Or Default,Grid Visualization,Byte Tracker,Image Convert Grayscale,Mask Area Measurement,Florence-2 Model,Buffer,SmolVLM2,SAM 3,Perception Encoder Embedding Model,Cache Get,SIFT,YOLO-World Model,Object Detection Model,Byte Tracker,Template Matching,Detections Consensus,Anthropic Claude,Property Definition,Google Gemini,Model Comparison Visualization,Detection Offset,QR Code Generator,EasyOCR,Image Slicer,S3 Sink,CLIP Embedding Model,Stability AI Inpainting,Segment Anything 2 Model,OpenAI,Pixel Color Count,Detections Filter,Roboflow Dataset Upload
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"
}