Buffer¶
Class: BufferBlockV1
Source: inference.core.workflows.core_steps.fusion.buffer.v1.BufferBlockV1
Maintain a sliding window buffer of the last N values by storing recent inputs in a FIFO (First-In-First-Out) queue, with newest elements added to the beginning and oldest elements automatically removed when the buffer exceeds the specified length, enabling temporal data collection, frame history tracking, batch processing preparation, and sliding window analysis workflows.
How This Block Works¶
This block maintains a rolling buffer that stores the most recent values passed to it, creating a sliding window of data over time. The block:
- Receives input data of any type (images, detections, values, etc.) and configuration parameters (buffer length and padding option)
- Maintains an internal buffer that persists across workflow executions:
- Buffer is initialized as an empty list when the block is first created
- Buffer state persists for the lifetime of the workflow execution
- Each buffer block instance maintains its own separate buffer
- Adds new data to the buffer:
- Inserts the newest value at the beginning (index 0) of the buffer array
- Most recent values appear first in the buffer
- Older values are shifted to later positions in the array
- Manages buffer size:
- When buffer length exceeds the specified
lengthparameter, removes the oldest elements - Keeps only the most recent
lengthvalues - Automatically maintains the sliding window size
- Applies optional padding:
- If
padis True: Fills the buffer withNonevalues until it reaches exactlylengthelements - Ensures consistent buffer size even when fewer than
lengthvalues have been received - If
padis False: Buffer size grows from 0 tolengthas values are added, then stays atlength - Returns the buffered array:
- Outputs a list containing the buffered values in order (newest first)
- List length equals
length(if padding enabled) or current buffer size (if padding disabled) - Values are ordered from most recent (index 0) to oldest (last index)
The buffer implements a sliding window pattern where new data enters at the front and old data exits at the back when capacity is reached. This creates a temporal history of recent values, useful for operations that need to look back at previous frames, detections, or measurements. The buffer works with any data type, making it flexible for images, detections, numeric values, or other workflow outputs.
Common Use Cases¶
- Frame History Tracking: Maintain a history of recent video frames for temporal analysis (e.g., track frame sequences, maintain recent image history, collect frames for comparison), enabling temporal frame analysis workflows
- Detection History: Buffer recent detections for trend analysis or comparison (e.g., track detection changes over time, compare current vs previous detections, analyze detection patterns), enabling detection history workflows
- Batch Processing Preparation: Collect multiple values before processing them together (e.g., batch process recent images, aggregate multiple detections, prepare data for batch operations), enabling batch processing workflows
- Sliding Window Analysis: Perform analysis on a rolling window of data (e.g., analyze trends over recent frames, calculate moving averages, detect changes in sequences), enabling sliding window analysis workflows
- Visualization Sequences: Maintain recent data for animation or sequence visualization (e.g., create frame sequences, visualize temporal changes, display recent history), enabling temporal visualization workflows
- Temporal Comparison: Compare current values with recent historical values (e.g., compare current frame with previous frames, detect changes over time, analyze temporal patterns), enabling temporal comparison workflows
Connecting to Other Blocks¶
This block receives data of any type and produces a buffered output array:
- After any block that produces values to buffer (e.g., buffer images from image sources, buffer detections from detection models, buffer values from analytics blocks), enabling data buffering workflows
- Before blocks that process arrays to provide batched or historical data (e.g., process buffered images, analyze detection arrays, work with value sequences), enabling array processing workflows
- Before visualization blocks to display sequences or temporal data (e.g., visualize frame sequences, display detection history, show temporal patterns), enabling temporal visualization workflows
- Before analysis blocks that require historical data (e.g., analyze trends over time, compare current vs historical, process temporal sequences), enabling temporal analysis workflows
- Before aggregation blocks to provide multiple values for aggregation (e.g., aggregate buffered values, process multiple detections, combine recent data), enabling aggregation workflows
- In temporal processing pipelines where maintaining recent history is required (e.g., track changes over time, maintain frame sequences, collect data for temporal analysis), enabling temporal processing workflows
Requirements¶
This block works with any data type (images, detections, values, etc.). The buffer maintains state across workflow executions within the same workflow instance. The length parameter determines the maximum number of values to keep in the buffer. When pad is enabled, the buffer will always return exactly length elements (padded with None if needed). When pad is disabled, the buffer grows from 0 to length elements as values are added, then maintains length elements by removing oldest values. The buffer persists for the lifetime of the workflow execution and resets when the workflow is restarted.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/buffer@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | โ |
length |
int |
Maximum number of elements to keep in the buffer. When the buffer exceeds this length, the oldest elements are automatically removed. Determines the size of the sliding window. Must be greater than 0. Typical values range from 2-10 for frame sequences, or higher for longer histories.. | โ |
pad |
bool |
Enable padding to maintain consistent buffer size. If True, the buffer is padded with None values until it reaches exactly length elements, ensuring the output always has length items even when fewer values have been received. If False, the buffer grows from 0 to length as values are added, then maintains length by removing oldest values. Use padding when downstream blocks require a fixed-size array.. |
โ |
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 Buffer in version v1.
- inputs:
S3 Sink,Email Notification,Clip Comparison,Morphological Transformation,Path Deviation,SAM 3,VLM As Detector,Keypoint Detection Model,Qwen-VL,Twilio SMS/MMS Notification,YOLO-World Model,Line Counter,Time in Zone,Polygon Zone Visualization,Stitch OCR Detections,MoonshotAI Kimi,OpenAI-Compatible LLM,OpenAI,VLM As Detector,Heatmap Visualization,Keypoint Visualization,Email Notification,Seg Preview,Anthropic Claude,Stability AI Image Generation,Google Vision OCR,Llama 3.2 Vision,Camera Focus,Label Visualization,Instance Segmentation Model,SAM 3,Path Deviation,Qwen3.5,Overlap Filter,Local File Sink,Multi-Label Classification Model,SmolVLM2,Google Gemini,Rate Limiter,Motion Detection,Byte Tracker,Background Color Visualization,Mask Edge Snap,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,Moondream2,Velocity,Grid Visualization,SIFT Comparison,Florence-2 Model,Delta Filter,Barcode Detection,Time in Zone,OCR Model,Detection Event Log,Single-Label Classification Model,VLM As Classifier,Detections Filter,Qwen2.5-VL,Detections Merge,First Non Empty Or Default,Detections Stabilizer,LMM For Classification,Keypoint Detection Model,Image Preprocessing,Roboflow Dataset Upload,SIFT,Dynamic Zone,Corner Visualization,Segment Anything 2 Model,Stability AI Outpainting,Halo Visualization,Multi-Label Classification Model,Qwen3-VL,Qwen3.5-VL,Time in Zone,Detections List Roll-Up,Blur Visualization,Semantic Segmentation Model,Property Definition,Perception Encoder Embedding Model,Distance Measurement,VLM As Classifier,Trace Visualization,Morphological Transformation,Stitch OCR Detections,Gaze Detection,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,JSON Parser,Pixel Color Count,Background Subtraction,QR Code Detection,Text Display,Detections Combine,Bounding Rectangle,ByteTrack Tracker,Absolute Static Crop,CSV Formatter,Florence-2 Model,Byte Tracker,Icon Visualization,Identify Outliers,Mask Area Measurement,Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Stability AI Inpainting,Image Convert Grayscale,Object Detection Model,Line Counter,QR Code Generator,OpenRouter,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Image Threshold,OC-SORT Tracker,Anthropic Claude,Dynamic Crop,Size Measurement,Detections Consensus,Clip Comparison,Cache Set,Dominant Color,Continue If,Contrast Enhancement,Bounding Box Visualization,Detection Offset,Depth Estimation,Keypoint Detection Model,CLIP Embedding Model,Image Contours,EasyOCR,Relative Static Crop,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Template Matching,Qwen 3.6 API,Single-Label Classification Model,Image Blur,Anthropic Claude,Per-Class Confidence Filter,Object Detection Model,Triangle Visualization,Roboflow Custom Metadata,OpenAI,SIFT Comparison,Slack Notification,Image Stack,Pixelate Visualization,Single-Label Classification Model,OpenAI,Stitch Images,Buffer,Instance Segmentation Model,Image Slicer,Environment Secrets Store,Line Counter Visualization,Image Slicer,Cosine Similarity,Detections Classes Replacement,Semantic Segmentation Model,Cache Get,LMM,Roboflow Dataset Upload,Expression,Detections Transformation,Color Visualization,Google Gemini,Data Aggregator,Classification Label Visualization,Camera Focus,Camera Calibration,Detections Stitch,Byte Tracker,Ellipse Visualization,PTZ Tracking (ONVIF),Identify Changes,SORT Tracker,Mask Visualization,GLM-OCR,Crop Visualization,Circle Visualization,CogVLM,Inner Workflow,Dimension Collapse,SAM2 Video Tracker,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma - outputs:
Object Detection Model,Perspective Correction,SAM 3,Email Notification,Keypoint Detection Model,Path Deviation,VLM As Detector,Qwen-VL,SAM 3,Clip Comparison,Object Detection Model,Twilio SMS/MMS Notification,Line Counter,OpenRouter,YOLO-World Model,OpenAI,Llama 3.2 Vision,Line Counter,Time in Zone,Polygon Zone Visualization,MoonshotAI Kimi,Anthropic Claude,OpenAI,VLM As Detector,Detections Consensus,Size Measurement,Email Notification,Keypoint Visualization,Seg Preview,Anthropic Claude,Llama 3.2 Vision,Clip Comparison,Cache Set,Label Visualization,SAM 3,Instance Segmentation Model,Path Deviation,Bounding Box Visualization,Google Gemini,Keypoint Detection Model,Motion Detection,Polygon Visualization,Google Gemma API,Qwen 3.6 API,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,Grid Visualization,Anthropic Claude,Florence-2 Model,Triangle Visualization,Time in Zone,Object Detection Model,OpenAI,VLM As Classifier,Instance Segmentation Model,Buffer,LMM For Classification,Keypoint Detection Model,Roboflow Dataset Upload,Line Counter Visualization,Detections Classes Replacement,Corner Visualization,Halo Visualization,Roboflow Dataset Upload,Time in Zone,Detections List Roll-Up,Color Visualization,Google Gemini,Classification Label Visualization,VLM As Classifier,Trace Visualization,Reference Path Visualization,Halo Visualization,Ellipse Visualization,Dot Visualization,Mask Visualization,Crop Visualization,Circle Visualization,Florence-2 Model,Webhook Sink,MoonshotAI Kimi,Google Gemma
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Buffer in version v1 has.
Bindings
-
input
data(Union[list_of_values,image,*]): Input data of any type to add to the buffer. Can be images, detections, values, or any other workflow output. Newest values are added to the beginning of the buffer array. The buffer maintains a sliding window of the most recent values..
-
output
output(list_of_values): List of values of any type.
Example JSON definition of step Buffer in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/buffer@v1",
"data": "$steps.visualization",
"length": 5,
"pad": true
}