5 AI Agent Memory Patterns That Actually Work (With Python Code)

5 AI Agent Memory Patterns That Actually Work (With Python Code) Every AI agent starts stateless. Each request is a blank slate. That works for simple tasks, but the moment you need context — "what did the user ask yesterday?" or "what files did I already review?" — you need memory. Here are 5 memory patterns, ordered from simplest to most powerful. Each one includes working code. Pattern 1: Conversation Buffer (The Default) Most frameworks give you this for free. Store the full conversation history and pass it back every time. class BufferMemory: def __init__(self, max_messages: int = 50): self.messages: list[dict] = [] self.max_messages = max_messages def add(self, role: str, content: str): self.messages.append({"role": role, "content": content}) # Trim from the front when we hit the limit if len(self.messages) > self.max_messages: self.messages = self.messages[-self.max_messages:] def get_context(self) -> list[dict]: return self.messages.copy() When to use: Simple chatbots, si