I’m working with the OpenAI ChatGPT API and I’m having trouble keeping the conversation flowing naturally. I expected that the user field would handle this automatically, but that’s not what’s happening.
When I send multiple requests to the API, each one seems to be treated as a completely separate conversation. The AI doesn’t remember what we talked about in previous messages. For example, if I ask “What’s the capital of France?” and then follow up with “What’s the population there?”, the second question doesn’t understand that “there” refers to Paris.
I’ve been looking at the API documentation but I’m still confused about the right way to handle this. Should I be storing the conversation history myself and sending it with each request? Or is there a built-in parameter I’m missing that maintains context between API calls?
Any help would be really appreciated because right now my chatbot feels very disconnected and doesn’t provide a good user experience.
omg, i totally faced this too! the api just forgets ur chat history, it’s super frustrating. i ended up making an array to keep track of everything and sent it every time. just be careful with tokens, or it’ll cost u a ton!
Yeah, this tripped me up when I started using the API too. OpenAI’s API doesn’t remember anything between calls - you’ve got to handle conversation memory yourself. I keep a messages array with the whole chat history, then add each new user message and response before the next API call. Token limits will bite you though (learned that one the hard way when longer chats started breaking). You’ll need a strategy to trim old messages - I usually keep the system prompt plus the last few exchanges, or summarize the older stuff. Don’t forget to tag your messages correctly as user, assistant, or system roles either.
yep, it’s true, the api doesn’t keep memory, so u gotta send all the convo history each time. also, if ur thread gets long, try to trim it to save on tokens.
The OpenAI API doesn’t remember anything between requests - each call is completely independent. You’ve got to handle conversation memory yourself by keeping a message array with the full chat history and sending it with every API call. Set it up with roles (system, user, assistant) and their content. Each new request needs all the previous messages included. This keeps the conversation flowing, but watch your token usage - longer chats eat more tokens and cost more. I made the same mistake with my first chatbot and got the exact same disconnected responses you’re seeing.
Yeah, the docs aren’t great on this - totally get the confusion. What you’re seeing is normal though. OpenAI’s API doesn’t store conversations at all. Every request is completely separate. I handle this by keeping my own conversation buffer with all the back-and-forth messages. You just need to structure your messages array right - system prompt first, then user and assistant messages in order. One thing I figured out the hard way: when you hit token limits, don’t just chop off old messages. Keep your system prompt and recent stuff, but cut out the middle parts of long conversations. Works way better.