I’m working with a LangChain setup that connects to a SQLite database and uses OpenAI’s ChatGPT API. When my API quota gets maxed out, the system keeps trying to reconnect automatically.
The error message I see is: “Retrying langchain.llms.openai.completion_with_retry…_completion_with_retry in 4.0 seconds as it raised RateLimitError: You exceeded your current quota, please check your plan and billing details.”
The system tries 5 times before giving up, which means I have to wait around 30-40 seconds before getting the final error. This makes it hard to handle the exception properly since it only gets thrown after all retry attempts are done.
Is there a way to change this retry behavior? I’m looking for options like:
- Reducing the number of retry attempts
- Setting a custom retry parameter
- Using some kind of callback to catch this earlier
- Overriding the error handling class
I’ve tried using debug mode to trace where this retry logic comes from but haven’t had much luck finding the right place to modify it.
The OpenAI library handles retries with exponential backoff automatically. You can skip this by setting max_retries=0 in your ChatOpenAI constructor, then add your own retry logic with full control over timing and error handling. Or pass a custom client instance through the openai_api_key parameter with modified retry settings. I’ve had good luck checking OpenAI’s usage API before making calls - it helps avoid rate limits entirely so you don’t need retries at all.
you could also wrap ur langchain calls in a custom decorator that catches the first RateLimitError and handles it right away instead of waiting through all the retries. if ur using certain versions, try setting exponential_base to 1 - it’ll cut down the backoff time between attempts.
Yes, you can customize the retry behavior when using OpenAI’s API with LangChain. You can modify the max_retries parameter in your ChatOpenAI instance to a lower value, such as 1 or 2, to decrease wait times. It’s also a good idea to implement a try-except block around your API calls so that you can catch the RateLimitError earlier and manage the retries according to your specific needs. Additionally, monitor your quota usage to help prevent hitting the limits unexpectedly.
you could also lower the request_timeout parameter to make it fail faster. or try hitting the OpenAI client directly instead of going through LangChain’s wrapper - sometimes skipping the middleware gives u better control over retry logic.
I had the same problem. You can subclass the OpenAI LLM class and modify the _completion_with_retry method to override the retry behavior. What worked for me was setting up a custom exception handler that catches RateLimitError before it hits the full retry cycle. Also check if your LangChain version supports the openai_api_base parameter with custom retry configs. I added a simple counter to track API calls per minute for quota monitoring - that way I avoid rate limits completely and skip the retry delays altogether.