Quick note on adding rate limit for AI agents using LiteLLM server
Briefly

The article discusses strategies to address 409 rate limit errors experienced while using AI frameworks, particularly with AWS Bedrock. It emphasizes the potential of a LiteLLM proxy server for better control over request rates for AI agents, rather than relying on the current setup of smolagents that respects max_tokens but still leads to overwhelming requests. By configuring a LiteLLM proxy server through Docker, the article presents a structured approach to effectively manage request limits for various AI models, suggesting that this method could avoid service interruptions and improve overall functionality.
Using a LiteLLM proxy server setup allows better control over request rates for AI agents, avoiding the 409 too many requests error by implementing rate limiting.
Currently, Huggingface smolagents respects max_tokens, but this doesn't sufficiently prevent rate limit issues; setting up a LiteLLM proxy can mitigate this problem fundamentally.
Read at Medium
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