How to handle rate limits when using multiple agents in parallel? #4078
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Had this exact problem when I built my multi-agent system. Here's what actually worked for me: The easiest fix is to use different API keys for different agents if you have them. But if you're on one key like I was, the solution is to use max_rpm in your agent config. You can set it like this: max_rpm=20 or max_rpm=30 in your Agent definition (just add it as a parameter alongside role and goal). This tells CrewAI to limit requests per minute and it'll automatically pace them so you don't hit OpenAI's limits. If you still hit rate limits even with max_rpm set, try switching from parallel to sequential processing - yeah it's slower but way more reliable. I only use parallel for truly independent tasks now. One more thing that helped me: if you're using GPT-4 for all agents, switch some of them to GPT-4o-mini. It's cheaper and has higher rate limits, so save GPT-4 for just your critical thinking agent. This combination of max_rpm limiting plus sequential processing plus mixing models basically solved all my rate limit issues. Hope this helps! |
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Had this exact problem when I built my multi-agent system. Here's what actually worked for me: The easiest fix is to use different API keys for different agents if you have them. But if you're on one key like I was, the solution is to use max_rpm in your agent config. You can set it like this: max_rpm=20 or max_rpm=30 in your Agent definition (just add it as a parameter alongside role and goal). This tells CrewAI to limit requests per minute and it'll automatically pace them so you don't hit OpenAI's limits. If you still hit rate limits even with max_rpm set, try switching from parallel to sequential processing - yeah it's slower but way more reliable. I only use parallel for truly indepe…