Rate limiting for AI agents is increasingly vital as service providers like AWS Bedrock impose strict request limits, leading to frequent errors. One approach is to directly request an increase in these rates. However, there’s a developing interest in implementing rate limiting from the agent side, allowing for more consistent operations. A LiteLLM proxy server is suggested for better control over requests by managing a configuration file to specify models and their respective request rates, thus optimizing interactions with various AI models.
The service providers, particularly AWS Bedrock, impose rate limits that can hinder the functioning of AI agents, prompting a strategy to manage these limits effectively.
To address frequent 409 error responses from service providers, the idea of implementing request rate limiting at the agent level is being explored, rather than just seeking to increase these limits.
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