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Multi-LLM Orchestration

Fallback and Retry Patterns

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Why Fallbacks Matter

Production Reality: Things Fail

In production, LLM APIs fail more often than you'd think:

  • Rate limits: Hit token or request limits
  • Timeouts: Model takes too long to respond
  • Server errors: 500, 502, 503 errors
  • Content filters: Request blocked by safety filters
  • Outages: Full API outages (rare but happen)

Retry Strategy: Exponential Backoff

Don't hammer the API when it's struggling. Use exponential backoff:

python

Fallback Chain Pattern

python

Error Classification

Error TypeShould Retry?Should Fallback?
Rate limit (429)Yes, with backoffYes, immediately
TimeoutYes, onceYes
Server error (5xx)Yes, with backoffAfter retries
Bad request (400)NoNo (fix the request)
Auth error (401/403)NoNo (fix credentials)