Fix LiteLLM rate limit error
Quick answer
A
RateLimitError in LiteLLM occurs when too many requests are sent too quickly. Add exponential backoff retry logic around your API calls to automatically handle RateLimitError and avoid failures. ERROR TYPE
api_error ⚡ QUICK FIX
Add exponential backoff retry logic around your API call to handle
RateLimitError automatically.Why this happens
LiteLLM enforces rate limits to prevent abuse and ensure fair usage. When your code sends requests too rapidly, the API returns a RateLimitError. This typically happens if you call the API in a tight loop without delays or retries.
Example of triggering code:
from litellm import Client
client = Client(api_key=os.environ["LITELLM_API_KEY"])
for _ in range(100):
response = client.chat("Hello") # Rapid calls cause rate limit error
print(response)Error output:
litellm.errors.RateLimitError: Too many requests, please slow down.The fix
Wrap your LiteLLM API calls with exponential backoff retry logic to catch RateLimitError and retry after a delay. This prevents your app from failing and respects the API's rate limits.
import os
import time
from litellm import Client, errors
client = Client(api_key=os.environ["LITELLM_API_KEY"])
max_retries = 5
base_delay = 1 # seconds
for _ in range(100):
for attempt in range(max_retries):
try:
response = client.chat("Hello")
print(response)
break # Success, exit retry loop
except errors.RateLimitError:
delay = base_delay * (2 ** attempt) # Exponential backoff
print(f"Rate limit hit, retrying in {delay} seconds...")
time.sleep(delay)
else:
print("Failed after retries due to rate limit.") output
Hello response text Rate limit hit, retrying in 1 seconds... Hello response text ...
Preventing it in production
- Implement retries with exponential backoff and jitter to avoid synchronized retries.
- Respect documented rate limits from LiteLLM API docs.
- Use client-side rate limiting or queue requests to smooth traffic.
- Monitor error rates and alert on spikes to adjust usage.
- Consider fallback models or degraded modes if rate limits persist.
Key Takeaways
- Use exponential backoff retries to handle LiteLLM rate limits gracefully.
- Monitor and respect API rate limits to prevent frequent errors.
- Implement client-side throttling and error alerts for production stability.