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