I’m working with the ChatGPT 3.5 turbo API and having trouble keeping the conversation context between different API requests. I originally thought the user parameter would handle this automatically, but that doesn’t seem to be working as expected.
When I make multiple API calls, each one seems to start fresh without any memory of previous messages in the conversation. This makes it impossible to have a coherent back-and-forth dialogue.
I need the AI to remember what we talked about earlier in the conversation so responses make sense in context. For example, if I ask about a specific topic and then follow up with “tell me more about that”, the API should understand what “that” refers to.
Has anyone figured out the correct approach for maintaining conversation state across multiple API requests? What parameters or techniques should I be using to achieve this?
Yeah, everyone’s right about storing message history, but manually managing conversation state gets messy quick. Been there.
With multiple users, session timeouts, and growing threads, you’re basically building a whole conversation management system. Database tables for messages, cleanup jobs, token counting, message trimming - it adds up.
I fixed this by setting up an automated workflow in Latenode. It handles conversation storage, manages token limits by summarizing old messages, and tracks multiple sessions without any database code from me.
The workflow triggers on each API call, grabs conversation history, adds the new message, calls ChatGPT with full context, then stores the response. Everything happens automatically.
Best part - when conversations get too long, it creates smart summaries of older messages so you never hit token limits but keep context. No more manual array management or memory leak worries.
ChatGPT 3.5 turbo doesn’t have memory across API requests, so you must manage the conversation history yourself. To maintain context, store all messages locally and include the complete message trail in your API calls. Each message should contain a role (user or assistant) and content fields, following the order they were sent. Be cautious about token limits; if you approach the limit, you may need to summarize or trim older messages while preserving the sequence for coherence.
Had this exact problem building a multi-turn chatbot for document analysis. Here’s what everyone’s missing: conversation persistence isn’t just storing messages - you need semantic continuity across sessions. I use a hybrid approach with raw message history plus contextual metadata. The messages array keeps the conversation flow, but I track topic threads and reference points separately. When users say “that” or “it,” I can inject clarifying context into the prompt. For token management, don’t just trim old messages. Create contextual anchors - brief summaries that preserve key reference points users might circle back to. Game changer for longer technical discussions where people reference earlier explanations constantly.
The user parameter doesn’t handle conversation history - it’s just for tracking different users in your app. You need to manually keep the full message history and send it with every API call. The API has no memory, so you’ve got to include the entire conversation context each time. I keep all messages in an array on my backend, add new user messages before calling the API, then toss the assistant’s response into that same array. Each request gets the complete chat history through the messages parameter. Just watch your token limits since longer chats eat more tokens per request.
yep, that got me too! the model doesn’t keep track of stuff, so u need to send the full chat history every time. i just keep an array of messages and send it all with each request.
Hit this same problem building a customer service bot last year. The stateless API totally caught me off guard at first. I solved it with a simple session manager that stores conversation threads in Redis with TTL. Here’s what nobody’s mentioning - you’ve got to handle the weird edge cases. Users bail mid-conversation all the time, and your context window will max out. I went with a sliding window setup: keep the system message, last few exchanges, and compress everything else into a summary. The hard part? Figuring out when to compress old messages without losing key context. My approach: save the first few messages and most recent ones, then summarize everything in between. Works for most situations.