Generating objects - AgentMark Docs

AgentMark generates structured objects with prompts that declare object_config in frontmatter and a JSON schema for the expected output. Object prompts use the same message-role tags as text prompts.

Example configuration

example.prompt.mdx

---
name: example
object_config:
  model_name: openai/gpt-5
  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>

OpenAI strict structured output requires additionalProperties: false on every object in your schema (the root and each nested object) and a required list naming all of that object’s properties. Zod’s z.object().strict() emits this for you; a hand-written or migrated JSON-schema schema block doesn’t, so it’s the most common Zod→frontmatter migration trap. Omit it and OpenAI rejects the request with a strict-schema validation error. (Providers without strict mode ignore the field, so it’s always safe to add.)

Tag	Description
`<System>`	System-level instructions
`<User>`	User message
`<Assistant>`	Assistant message (optional; include for few-shot examples)

Using schema references

Instead of writing a full schema inline, you can extract it into a .json file and use $ref to reference it. AgentMark resolves the reference at build time.

extract-event.prompt.mdx

---
name: extract-event
object_config:
  model_name: openai/gpt-5
  schema:
    $ref: ./schemas/event.json
---

<System>You are an event planner that creates detailed event objects.</System>
<User>Create an event for a team meeting next Friday with John as the facilitator.</User>

See Schema references for full documentation on $ref syntax, transitive references, and JSON Pointer fragments.

Available configuration

Property	Type	Description	Required
`model_name`	`string`	The name of the model to use for object generation.	Yes
`schema`	`JSONSchema`	Schema defining the expected structure of the model’s output. Supports `$ref` for reusable definitions.	Yes
`max_tokens`	`number`	Maximum number of tokens to generate.	No
`temperature`	`number`	Controls the randomness of the output; higher values are more random.	No
`max_calls`	`number`	Maximum number of LLM calls allowed.	No
`top_p`	`number`	Cumulative probability for nucleus sampling.	No
`top_k`	`number`	Limits next-token selection to the top `k` tokens.	No
`presence_penalty`	`number`	Penalizes tokens based on presence in the text so far.	No
`frequency_penalty`	`number`	Penalizes tokens based on frequency in the text so far.	No
`stop_sequences`	`string[]`	Strings that, if encountered, stop generation.	No
`seed`	`number`	Random-number seed for reproducibility.	No
`max_retries`	`number`	Maximum number of retries on failure.	No
`schema_name`	`string`	Name sent with the schema (used by OpenAI structured outputs).	No
`schema_description`	`string`	Description sent with the schema.	No
`tools`	`string[]`	List of tool names or MCP URIs available to the model. You resolve these names to implementations at your call site (see Tools and agents).	No

Running an object prompt

See the SDK usage section of Running prompts (under the Local tab) for the object-generation SDK code patterns: render the prompt with prompt.format(), then make your own structured-output call (generateObject / streamObject in TypeScript, the OpenAI client in Python).

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​Example configuration

​Tags

​Using schema references

​Available configuration

​Running an object prompt

​Have questions?

Example configuration

Tags

Using schema references

Available configuration

Running an object prompt

Have questions?