> ## Documentation Index
> Fetch the complete documentation index at: https://docs.threetone.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Models

> Choose a managed language model, review supported generation settings, and save a configuration with compatible backups.

Choose a language model for the work your agent needs to do: understand the request, follow the prompt, and produce an appropriate response. Start with the current model catalog so the identifier and available settings match Threetone.

<Note>
  Creation and draft saving are separate from publication. Later publication checks model availability and compatibility. Managed text-only configurations can pass those checks; voice and custom-model publication are currently unavailable. See [current availability](/agents-platform/overview#current-availability).
</Note>

## Choose and review a model

<Steps>
  <Step title="Check the catalog">
    Read [Models](/overview/models) and [List available LLMs](/api-reference/agents-platform/list-available-llms). Choose an available managed model and keep its exact identifier. A provider's model name is not necessarily its Threetone catalog identifier.
  </Step>

  <Step title="Set the primary model">
    In the agent editor, open **LLM** to review the model and its settings. The saved primary model is `conversation_config.agent.prompt.llm`.

    For API authoring, read the intended branch first and preserve the rest of its configuration. Use an explicit `branch_id` when updating conversation settings.
  </Step>

  <Step title="Review supported settings">
    Set only options supported by the chosen model. Use the table below to locate them, and review [LLM cascading](/agents-platform/advanced/llm-cascading) before selecting backups.
  </Step>

  <Step title="Save and read back">
    Save a complete [server draft](/agents-platform/deploy/versioning). If you publish an eligible managed text-only configuration, review the diff and record the new version ID. Read the saved document with [Get agent](/api-reference/agents-platform/get-agent) and check the primary model and backup policy together.
  </Step>
</Steps>

## Generation settings

These fields belong to `conversation_config.agent.prompt`.

| Field | What to review |
| - | - |
| `llm` | Exact managed model identifier from the current catalog. |
| `temperature` | Whether the selected model accepts the requested sampling setting. |
| `max_tokens` | The response budget, subject to the selected model's limits. |
| `reasoning_effort`, `thinking_budget` | Model-specific reasoning controls; support is not universal. |
| `enable_reasoning_summary` | Whether the model supports this requested output setting. |
| `backup_llm_config` | The selected backup policy and compatibility of every resolved backup. |
| `cascade_timeout_seconds` | The authored timeout setting associated with cascading. |

A successful save is evidence of configuration storage. It does not measure response quality, latency, or successful failover. Prepare a small set of representative prompts before comparing models when execution is available.

## Frequently asked questions

<AccordionGroup>
  <Accordion title="Why does publication reject a model that creation accepted?" id="faq-model-publication">
    Creation stores the initial authoring configuration. Later publication also checks available models and compiles an execution configuration. Check the primary model, backups, and whether the agent is text-only.
  </Accordion>

  <Accordion title="Can I connect my own model endpoint?" id="faq-model-custom">
    You can prepare a [custom model configuration](/agents-platform/advanced/custom-models/overview), but custom-model publication is unavailable. An endpoint probe does not remove that restriction.
  </Accordion>
</AccordionGroup>


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.