Tips and Tricks #64: Implement Retry Logic for LLM API Calls

Handle rate limits and transient failures gracefully with exponential backoff.

Code Snippet

import time
import random
from functools import wraps
from openai import RateLimitError, APIError

def retry_with_backoff(max_retries=3, base_delay=1):
    """Decorator for retrying LLM calls with exponential backoff."""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except RateLimitError:
                    if attempt == max_retries - 1:
                        raise
                    delay = base_delay * (2 ** attempt) + random.uniform(0, 1)
                    print(f"Rate limited. Retrying in {delay:.2f}s...")
                    time.sleep(delay)
                except APIError as e:
                    if attempt == max_retries - 1:
                        raise
                    print(f"API error: {e}. Retrying...")
                    time.sleep(base_delay)
            return None
        return wrapper
    return decorator

@retry_with_backoff(max_retries=5, base_delay=2)
def call_llm(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

Why This Helps

• Handles rate limits automatically
• Improves reliability of LLM integrations
• Prevents cascading failures

How to Test

• Simulate rate limit errors
• Verify exponential backoff timing

When to Use

All production LLM integrations. Essential for high-volume applications.

Performance/Security Notes

Add jitter to prevent thundering herd. Log retries for monitoring.

References

• https://platform.openai.com/docs/guides/rate-limits

Try this tip in your next project and share your results in the comments!