LiteLLM retries cache rate limits

If you want LiteLLM to handle retries, cache, and rate limits, there are a few different layers involved:

1) Retries

LiteLLM can retry failed requests, but the exact behavior depends on how you’re calling it.

Typical approach:

• configure the num_retries / retry settings in the LiteLLM client or proxy
• retry on transient errors like 429, 500, timeouts

Example pattern:

import litellm

response = litellm.completion(
    model="openai/gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello"}],
    num_retries=3,
)

2) Cache

LiteLLM supports caching so repeated identical requests can be served without hitting the provider again.

Common options:

• in-memory cache
• Redis cache
• prompt/response caching via proxy or SDK settings

Example concept:

from litellm import completion
from litellm.caching import Cache

litellm.cache = Cache(type="redis")

Caching is useful to reduce:

• latency
• token usage
• provider quota consumption

3) Rate limits

There are two kinds of rate limits:

• Provider-side limits (OpenAI, Anthropic, etc.)
• Your own app-side limits

LiteLLM can help with provider throttling by:

• retrying on 429
• routing across fallbacks
• using proxy-level rate limiting / budgets

If you’re using the LiteLLM Proxy, you can also configure:

• per-model RPM/TPM limits
• budgets
• fallback models

Practical recommendation

A good setup is:

1. Enable cache for repeated prompts
2. Set retries for transient failures
3. Use rate limiting / fallback models to avoid exhausting one provider
4. Log 429s so you can see whether you need more aggressive throttling

Example strategy

• cache identical requests for 1 hour
• retry up to 3 times with exponential backoff
• on 429, switch to a fallback model/provider
• optionally enforce local RPM limits before sending requests

If you want, I can give you:

1. a LiteLLM Python SDK example, or
2. a LiteLLM Proxy config showing retries + cache + rate limits together.