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