Evidence-first authority page
AI Agent Rate Limiting & Retry Strategies
How to handle LLM API rate limits gracefully in an agent, and why naive retry logic can make the problem worse.
Direct answer
Handle LLM API rate limits with exponential backoff and jitter on retries, respect the provider's rate-limit headers where available, and queue or throttle requests proactively at high volume rather than relying solely on reactive retries — naive immediate-retry logic on a rate-limit error can synchronize retries across concurrent requests and make the rate-limit condition worse, not better.
Last reviewed 2026-08-29
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Why naive immediate retries backfire
If multiple concurrent requests all hit a rate limit at once and all retry immediately, they collide again at the same moment, potentially extending the rate-limited period rather than recovering from it. Exponential backoff (waiting progressively longer between retries) combined with jitter (randomizing the exact wait time) spreads retries out and avoids this synchronized-retry failure pattern.
Proactive throttling beats reactive retrying at scale
At meaningful request volume, proactively limiting your own request rate to stay under the provider's documented limit is more reliable than depending on retry logic to recover after hitting the limit — a request queue with a controlled dispatch rate prevents the rate-limit condition from occurring in the first place, rather than reacting to it after the fact.
Distinguish rate limits from other errors
A rate-limit error (typically HTTP 429) should be retried; a client error from malformed input (typically 400) should not be — retrying a malformed request indefinitely just wastes time and quota without ever succeeding. Handle these as genuinely different error categories in your retry logic rather than retrying every failure identically.