Agents and provider wiring

Read model docs and the installed package source. Omitting model uses SDK defaults, which can change (0.22.3 fallback: gpt-5.6-luna, not a model recommendation); OPENAI_DEFAULT_MODEL is an SDK setting, while a custom app variable needs explicit wiring. Tuned default ModelSettings apply in 0.22.3 only to supported gpt-5 names; other IDs, including gpt-6.1-sol, get a plain ModelSettings() and the API's default reasoning effort, so set reasoning explicitly. The installed agents/models/default_models.py shows the current rules.

Use the current API model catalog and model-selection guidance for a new choice; check account/provider access and the chosen model's API page.

Configure only settings supported by the selected provider/model. Reasoning, sampling and tool support differ by endpoint. Do not infer capabilities or a model family from an Azure deployment's user-defined name.

Azure can use native OpenAI model classes with an AsyncAzureOpenAI client: per agent via OpenAIResponsesModel or OpenAIChatCompletionsModel(model=<deployment>, openai_client=client), or process-wide via set_default_openai_client(client, use_for_tracing=False) plus set_default_openai_api("chat_completions") when the deployment lacks Responses. Choose Responses/Chat Completions from deployed capabilities and the current Azure API contract. GPT-6 Astra and GPT-6.1 Sol require Responses for tool calling; GPT-6 Sol and Luna allow function calling on Chat Completions only with reasoning effort none; an agent needing tools and reasoning must use Responses (LitellmModel always uses Chat Completions). See the GPT-6 API guide and the Azure deployment's capabilities before choosing an adapter. Read deployment configuration or documented Azure management APIs to identify the deployment; do not rely on an old generic listing endpoint.

LiteLLM/Any-LLM are beta adapters recommended when built-in integration points are insufficient. They have independent model maps, imports and package extras. A rejected parameter may be adapter capability metadata rather than the provider: LiteLLM raises UnsupportedParamsError ... ['reasoning_effort'] for an Azure deployment name its model map does not recognize. After confirming the deployment supports it, pass ModelSettings(reasoning=..., extra_args={"allowed_openai_params": ["reasoning_effort"]}); litellm.drop_params=True silently discards the setting.

Tracing is separate from model authentication. An Azure/LiteLLM key does not authorize OpenAI trace uploads. Configure trace destination or disable uploads according to the application's policy.

See SDK models, LiteLLM integration and the configured provider's current docs.