> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentmark.co/llms.txt
> Use this file to discover all available pages before exploring further.
# Generating objects
> Generate structured JSON objects from prompts with schema validation using AgentMark.
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
```jsx example.prompt.mdx theme={null}
---
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
---
You are an event planner that creates detailed event objects with attendees and their roles.
Create an event for a team meeting next Friday with John as the facilitator and Sarah as the note-taker.
```
**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.)
## Tags
| Tag | Description |
| ------------- | ----------------------------------------------------------- |
| `` | System-level instructions |
| `` | User message |
| `` | 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.
```jsx extract-event.prompt.mdx theme={null}
---
name: extract-event
object_config:
model_name: openai/gpt-5
schema:
$ref: ./schemas/event.json
---
You are an event planner that creates detailed event objects.
Create an event for a team meeting next Friday with John as the facilitator.
```
See [Schema references](/build/schema-references) for full documentation on `$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`](/build/schema-references) 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](/build/tools-and-agents)). | No |
## Running an object prompt
See the **SDK usage** section of [Running prompts](/build/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).
Have questions?
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