Anthropic Claude

Class: AnthropicClaudeBlockV1

Source: inference.core.workflows.core_steps.models.foundation.anthropic_claude.v1.AnthropicClaudeBlockV1

Ask a question to Anthropic Claude model with vision capabilities.

You can specify arbitrary text prompts or predefined ones, the block supports the following types of prompt:

• Open Prompt (unconstrained) - Use any prompt to generate a raw response

• Text Recognition (OCR) (ocr) - Model recognizes text in the image

• Visual Question Answering (visual-question-answering) - Model answers the question you submit in the prompt

• Captioning (short) (caption) - Model provides a short description of the image

• Captioning (detailed-caption) - Model provides a long description of the image

• Single-Label Classification (classification) - Model classifies the image content as one of the provided classes

• Multi-Label Classification (multi-label-classification) - Model classifies the image content as one or more of the provided classes

• Unprompted Object Detection (object-detection) - Model detects and returns the bounding boxes for prominent objects in the image

• Structured Output Generation (structured-answering) - Model returns a JSON response with the specified fields

You need to provide your Anthropic API key to use the Claude model.

Type identifier

Use the following identifier in step "type" field: roboflow_core/anthropic_claude@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
task_type	str	Task type to be performed by model. Value determines required parameters and output response..	❌
prompt	str	Text prompt to the Claude model.	✅
output_structure	Dict[str, str]	Dictionary with structure of expected JSON response.	❌
classes	List[str]	List of classes to be used.	✅
api_key	str	Your Anthropic API key.	✅
model_version	str	Model to be used.	✅
max_tokens	int	Maximum number of tokens the model can generate in it's response..	❌
temperature	float	Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..	✅
max_image_size	int	Maximum size of the image - if input has larger side, it will be downscaled, keeping aspect ratio.	✅
max_concurrent_requests	int	Number of concurrent requests that can be executed by block when batch of input images provided. If not given - block defaults to value configured globally in Workflows Execution Engine. Please restrict if you hit Anthropic API limits..	❌

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 Anthropic Claude in version v1.

• inputs: Crop Visualization, Keypoint Detection Model, Ellipse Visualization, VLM as Classifier, Stability AI Inpainting, Stability AI Image Generation, Blur Visualization, Circle Visualization, Pixelate Visualization, Model Comparison Visualization, Webhook Sink, Stability AI Outpainting, Bounding Box Visualization, Identify Changes, Grid Visualization, Cosine Similarity, Background Color Visualization, Llama 3.2 Vision, Image Contours, LMM, Object Detection Model, Twilio SMS Notification, Label Visualization, Triangle Visualization, Dynamic Crop, Florence-2 Model, Google Vision OCR, Florence-2 Model, Keypoint Visualization, OCR Model, Halo Visualization, Corner Visualization, Line Counter Visualization, LMM For Classification, Template Matching, Dot Visualization, Size Measurement, Clip Comparison, Anthropic Claude, Slack Notification, Roboflow Dataset Upload, Icon Visualization, Roboflow Custom Metadata, Depth Estimation, Distance Measurement, CogVLM, Polygon Zone Visualization, Stitch Images, Image Slicer, OpenAI, Email Notification, Relative Static Crop, Google Gemini, SIFT Comparison, SIFT Comparison, Line Counter, Dimension Collapse, Buffer, Pixel Color Count, Image Slicer, Gaze Detection, Image Threshold, Perspective Correction, Camera Focus, Camera Calibration, Classification Label Visualization, Reference Path Visualization, Color Visualization, Model Monitoring Inference Aggregator, Instance Segmentation Model, Local File Sink, OpenAI, Clip Comparison, Roboflow Dataset Upload, Mask Visualization, QR Code Generator, OpenAI, CSV Formatter, Stitch OCR Detections, SIFT, Polygon Visualization, Image Convert Grayscale, VLM as Detector, Dynamic Zone, Image Blur, Multi-Label Classification Model, Trace Visualization, Line Counter, Absolute Static Crop, Image Preprocessing, Single-Label Classification Model
• outputs: Crop Visualization, Keypoint Detection Model, Ellipse Visualization, VLM as Classifier, Stability AI Inpainting, Stability AI Image Generation, Keypoint Detection Model, Cache Get, Circle Visualization, YOLO-World Model, Model Comparison Visualization, Webhook Sink, Stability AI Outpainting, Detections Consensus, Bounding Box Visualization, Time in Zone, Time in Zone, Path Deviation, Grid Visualization, VLM as Classifier, Background Color Visualization, Llama 3.2 Vision, LMM, Object Detection Model, Twilio SMS Notification, Label Visualization, Triangle Visualization, Dynamic Crop, Florence-2 Model, Detections Stitch, VLM as Detector, Instance Segmentation Model, Florence-2 Model, Keypoint Visualization, Halo Visualization, PTZ Tracking (ONVIF).md), Corner Visualization, Line Counter Visualization, LMM For Classification, CLIP Embedding Model, Time in Zone, Perception Encoder Embedding Model, Dot Visualization, Size Measurement, Clip Comparison, Anthropic Claude, Slack Notification, Roboflow Dataset Upload, Icon Visualization, Cache Set, Roboflow Custom Metadata, Distance Measurement, CogVLM, Polygon Zone Visualization, OpenAI, Email Notification, Segment Anything 2 Model, Detections Classes Replacement, Google Gemini, SIFT Comparison, Line Counter, Pixel Color Count, Buffer, Image Threshold, Perspective Correction, JSON Parser, Classification Label Visualization, Path Deviation, Reference Path Visualization, Local File Sink, OpenAI, Instance Segmentation Model, Color Visualization, Model Monitoring Inference Aggregator, Clip Comparison, Roboflow Dataset Upload, Mask Visualization, QR Code Generator, OpenAI, Polygon Visualization, VLM as Detector, Moondream2, Image Blur, Trace Visualization, Line Counter, Google Vision OCR, Image Preprocessing, Object Detection Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Anthropic Claude in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • prompt (string): Text prompt to the Claude model.
  • classes (list_of_values): List of classes to be used.
  • api_key (Union[string, secret]): Your Anthropic API key.
  • model_version (string): Model to be used.
  • temperature (float): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..
  • max_image_size (integer): Maximum size of the image - if input has larger side, it will be downscaled, keeping aspect ratio.

• output

  • output (Union[string, language_model_output]): String value if string or LLM / VLM output if language_model_output.
  • classes (list_of_values): List of values of any type.

Example JSON definition of step Anthropic Claude in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/anthropic_claude@v1",
    "images": "$inputs.image",
    "task_type": "<block_does_not_provide_example>",
    "prompt": "my prompt",
    "output_structure": {
        "my_key": "description"
    },
    "classes": [
        "class-a",
        "class-b"
    ],
    "api_key": "xxx-xxx",
    "model_version": "claude-sonnet-4",
    "max_tokens": "<block_does_not_provide_example>",
    "temperature": "<block_does_not_provide_example>",
    "max_image_size": "<block_does_not_provide_example>",
    "max_concurrent_requests": "<block_does_not_provide_example>"
}