Building Conversational Agents with Memory

A single API call is stateless — the model has no memory of previous interactions. To build conversational agents that remember context, you need explicit memory management. This lesson covers conversation history, windowing strategies, summarisation, and persistent memory stores.

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The Memory Problem

LLMs are stateless. Every API call is independent. The model only "remembers" what you include in the messages array.

Call 1: "My name is Alice."       → "Hello, Alice!"
Call 2: "What's my name?"         → "I don't know your name."
                                     (no context from Call 1)

To maintain conversation continuity, you must manage the message history yourself.

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Basic Conversation History

The simplest approach: keep all messages in a list.

from openai import OpenAI

client = OpenAI()

class ChatBot:
    def __init__(self, system_prompt: str):
        self.messages = [
            {"role": "system", "content": system_prompt}
        ]

    def chat(self, user_input: str) -> str:
        self.messages.append({"role": "user", "content": user_input})

        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=self.messages,
        )

        assistant_msg = response.choices[0].message.content
        self.messages.append({"role": "assistant", "content": assistant_msg})
        return assistant_msg

bot = ChatBot("You are a helpful maths tutor.")
print(bot.chat("What is 2 + 2?"))      # "4"
print(bot.chat("Now multiply by 10.")) # "40" — remembers previous context