effect-uai-multi-model-fallback

To handle rate limit errors with language models, you can use a multi-tier fallback loop that catches retryable failures and automatically switches to an alternate provider tier while preserving the conversation history.

Multi-model fallback is a technique that tries a primary AI provider first and automatically advances to the next tier on RateLimited or Unavailable errors, exhausting tiers gracefully while reusing the same conversation state.

To set up provider fallback tiers, define your tiers with model and provider details, then run a loop that advances to the next tier automatically when the current tier encounters a retryable failure like a rate limit.

Yes, you can preserve conversation history when switching to a fallback AI provider by applying the fallback mechanism to reuse the same conversation state and context across all defined tiers.

When non-retryable failures occur during multi-model fallback, the error propagates directly to the callers rather than triggering a retry, ensuring that only rate-limited or unavailable errors advance to the next tier.