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Agentmark supports generating objects using object prompts. Object prompts are defined by specifying object_config in the object prompt’s frontmatter and defining schema for the object.

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Example Configuration

example.prompt.mdx
---
name: example
object_config:
  model_name: gpt-4
  schema:
    type: object
    properties:
      event:
        type: object
        properties:
          name: 
            type: string
            description: The name of the event
          date:
            type: string
            description: The date of the event
          attendees:
            type: array
            items:
              type: object
              properties:
                name:
                  type: string
                  description: The name of the attendee
                role:
                  type: string
                  description: The role of the attendee
              required:
                - name
                - role
        required: 
          - name
          - date
          - attendees
---

<System>You are an event planner that creates detailed event objects with attendees and their roles.</System>
<User>Create an event for a team meeting next Friday with John as the facilitator and Sarah as the note-taker.</User>

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Tags

TagDescription
<System>System-level instructions
<User>User message
<Assistant>Assistant message

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Available Configuration

PropertyTypeDescriptionOptional/Required
model_namestringThe name of the model to use for object generation.Required
max_tokensnumberMaximum number of tokens to generate.Optional
temperaturenumberControls the randomness of the output; higher values result in more random outputs.Optional
max_callsnumberMaximum number of LLM calls allowed.Optional
top_pnumberControls the cumulative probability for nucleus sampling.Optional
top_knumberLimits the next token selection to the top k tokens.Optional
presence_penaltynumberPenalizes new tokens based on their presence in the text so far, encouraging the model to discuss new topics.Optional
frequency_penaltynumberPenalizes new tokens based on their frequency in the text so far, reducing the likelihood of repeating the same line verbatim.Optional
stop_sequencesstring[]Array of strings where the generation will stop if any of the strings are encountered.Optional
seednumberSeed value for random number generation, ensuring reproducibility.Optional
max_retriesnumberMaximum number of retries for the request in case of failures.Optional
schemaJSONSchemaA schema defining the expected structure of the model’s output.Required
schema_namestringA name for the schema.Optional
schema_descriptionstringA description of the schema.Optional
toolsstring[]List of tool names and MCP URIs available for the model to use. Tools are resolved from the tools passed to createAgentMarkClient.Optional

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