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:
Twilio SMS Notification,Model Monitoring Inference Aggregator,Local File Sink,Google Gemini,Roboflow Dataset Upload,Roboflow Dataset Upload,Object Detection Model,Email Notification,Google Gemma API,OCR Model,Google Vision OCR,Twilio SMS/MMS Notification,Google Gemini,Instance Segmentation Model,EasyOCR,Anthropic Claude,OpenAI,Clip Comparison,Anthropic Claude,CSV Formatter,VLM As Detector,LMM,Webhook Sink,Anthropic Claude,Stitch OCR Detections,Roboflow Custom Metadata,Multi-Label Classification Model,Qwen 3.5 API,OpenAI,Single-Label Classification Model,OpenAI,Google Gemini,VLM As Classifier,Roboflow Vision Events,Qwen3.5-VL,Qwen 3.6 API,Florence-2 Model,LMM For Classification,CogVLM,Florence-2 Model,OpenAI,Keypoint Detection Model,MoonshotAI Kimi,Llama 3.2 Vision,GLM-OCR,Email Notification,S3 Sink,Slack Notification,Stitch OCR Detections - outputs:
Roboflow Dataset Upload,Line Counter Visualization,Mask Edge Snap,OCR Model,Image Slicer,Gaze Detection,Qwen2.5-VL,Instance Segmentation Model,Distance Measurement,Color Visualization,Multi-Label Classification Model,Bounding Rectangle,ByteTrack Tracker,Polygon Visualization,Ellipse Visualization,Single-Label Classification Model,Data Aggregator,Relative Static Crop,Byte Tracker,Detections Consensus,Barcode Detection,Detections Classes Replacement,Cache Set,Webhook Sink,Continue If,Trace Visualization,Object Detection Model,Qwen 3.5 API,Camera Focus,Stitch OCR Detections,OpenAI,Buffer,SAM 3,Size Measurement,Image Threshold,Heatmap Visualization,SORT Tracker,Florence-2 Model,Halo Visualization,Detections Transformation,Path Deviation,GLM-OCR,Dot Visualization,S3 Sink,Path Deviation,Semantic Segmentation Model,Seg Preview,Twilio SMS Notification,Model Monitoring Inference Aggregator,Google Gemini,Roboflow Dataset Upload,Dynamic Zone,Clip Comparison,VLM As Classifier,Pixelate Visualization,Line Counter,Twilio SMS/MMS Notification,Polygon Zone Visualization,Motion Detection,Blur Visualization,Background Subtraction,Text Display,CSV Formatter,Stability AI Image Generation,Detections Merge,Perspective Correction,Anthropic Claude,Line Counter,Bounding Box Visualization,Velocity,Depth Estimation,Overlap Filter,Rate Limiter,Stability AI Inpainting,Polygon Visualization,SmolVLM2,SIFT,Roboflow Vision Events,VLM As Detector,Google Gemini,Label Visualization,Expression,Grid Visualization,Qwen3.5-VL,Per-Class Confidence Filter,Contrast Equalization,Triangle Visualization,Property Definition,Halo Visualization,Circle Visualization,Segment Anything 2 Model,Mask Visualization,Dominant Color,OpenAI,MoonshotAI Kimi,Llama 3.2 Vision,Email Notification,Slack Notification,CLIP Embedding Model,Detections Stitch,Detections Stabilizer,Object Detection Model,Stability AI Outpainting,Google Gemma API,Email Notification,Google Vision OCR,Identify Outliers,Google Gemini,Image Preprocessing,EasyOCR,Detections Combine,Object Detection Model,Cosine Similarity,SAM2 Video Tracker,Detection Event Log,Byte Tracker,OpenAI,Anthropic Claude,Time in Zone,Qwen3-VL,Model Comparison Visualization,Roboflow Custom Metadata,YOLO-World Model,Inner Workflow,Detection Offset,Instance Segmentation Model,Perception Encoder Embedding Model,Semantic Segmentation Model,Single-Label Classification Model,Detections List Roll-Up,VLM As Classifier,Template Matching,Mask Area Measurement,Stitch Images,Qwen 3.6 API,SIFT Comparison,Morphological Transformation,Instance Segmentation Model,CogVLM,Crop Visualization,Florence-2 Model,Camera Calibration,Multi-Label Classification Model,Time in Zone,OC-SORT Tracker,SAM 3,QR Code Detection,Local File Sink,Icon Visualization,Detections Filter,First Non Empty Or Default,Image Contours,JSON Parser,Keypoint Detection Model,Time in Zone,Reference Path Visualization,Dimension Collapse,Anthropic Claude,Clip Comparison,VLM As Detector,LMM,Pixel Color Count,Identify Changes,Classification Label Visualization,Image Slicer,Absolute Static Crop,Byte Tracker,Image Blur,Multi-Label Classification Model,Image Convert Grayscale,SAM 3,Single-Label Classification Model,OpenAI,Corner Visualization,Delta Filter,Keypoint Detection Model,Dynamic Crop,Keypoint Visualization,Moondream2,Cache Get,QR Code Generator,Camera Focus,LMM For Classification,Morphological Transformation,Keypoint Detection Model,Contrast Enhancement,Background Color Visualization,PTZ Tracking (ONVIF),Stitch OCR Detections,SIFT Comparison
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"
}