pydantic-ai-model-integration

Configure fallback model chains by specifying primary and secondary providers—such as GPT-4o with Claude Sonnet as backup—so requests automatically retry with the next provider if the first fails. This ensures reliable responses across OpenAI, Anthropic, Google, and other providers without manual intervention.

Yes, streaming responses work consistently across providers with unified model settings. Configure streaming behavior once and apply it across OpenAI, Anthropic, Google, and other providers to handle real-time output reliably.

Monitor per-run and total usage metrics across providers to track consumption and optimize API calls. This reveals which providers and models consume the most resources, helping you balance cost and performance.

Use dynamic model selection to choose providers and models based on specific needs—such as selecting GPT-4o for complex reasoning or a faster model for simple tasks. Apply provider-specific features and structured output requirements per request.

Yes, deferred model checks allow you to validate model availability and settings at runtime rather than upfront, enabling flexible deployments where model choices depend on runtime conditions or user input.

Structured output is supported across providers with consistent configuration, allowing you to enforce response schemas across OpenAI, Anthropic, Google, and others to ensure reliable, parseable results.