SpaceXAI

Run SpaceXAI Grok models with vision capabilities.

Ask a question to SpaceXAI Grok models 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

For object-detection, the model returns a JSON list of {"label": ..., "box_2d": [x_min, y_min, x_max, y_max]} entries, where coordinates are percentages of image width and height. Use VLM As Detector with model_type set to spacexai to turn the output into standard detection predictions.

You need to provide your own xAI API key (created at console.x.ai) to use this block. Requests go directly to xAI and are billed to your xAI account.

Type identifier

Use the following identifier in step "type" field: roboflow_core/spacexai@v1 to add the block as a 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 Grok 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 xAI API key.	✅
model_version	str	Model to be used.	✅
reasoning_effort	str	Optional reasoning effort passed to xAI.	✅
max_tokens	int	Maximum number of tokens the model can generate in its response. If not specified, the model will use its default limit. Minimum value is 16..	❌
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_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 xAI limits..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Runtime compatibility

requires_internet - air-gapped / offline deployments : This block depends on a service that is not reachable from fully offline / air-gapped deployments.

Input and Output Bindings

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

Input and output bindings

• input

  • images (image): The image to infer on..

  • prompt (string): Text prompt to the Grok model.

  • classes (list_of_values): List of classes to be used.

  • api_key (Union[secret, string]): Your xAI API key.

  • model_version (string): Model to be used.

  • reasoning_effort (string): Optional reasoning effort passed to xAI.

  • temperature (float): Temperature to sample from the model - value in range 0.0-2.0, the higher - the more random / "creative" the generations are..

• 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

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/spacexai@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": "grok-4.6",
	    "reasoning_effort": "<block_does_not_provide_example>",
	    "max_tokens": "<block_does_not_provide_example>",
	    "temperature": "<block_does_not_provide_example>",
	    "max_concurrent_requests": "<block_does_not_provide_example>"
	}