Ai Agent Memory System Design

Memory System Design

Human memory is divided into short-term memory and long-term memory, and the memory system of AI Agents adopts a similar design.

Understanding the differences and uses of these two types of memory is the foundation for building intelligent Agents.

Short-Term Memory: Agent's Working Memory

Short-term memory is like the Agent's workbench, storing the context of the current conversation and temporary information.

Features:

Limited capacity: Usually can only save the most recent few conversations (e.g., the last 10-20 turns)
Fast access: Reading and writing speeds are very fast
Temporary: Usually cleared or compressed after the conversation ends
Context-dependent: Directly affects current response generation

Main Uses:

Maintaining conversation coherence
Storing intermediate results of current tasks
Remembering user preferences mentioned in the current conversation

Technical Implementation:

class ShortTermMemory:\n    """Short-term memory implementation"""\n    \n    def __init__(self, max_turns=10):\n        self.max_turns = max_turns  # Maximum conversation turns\n        self.conversation_history = []  # Conversation history\n        self.temporary_data = {}  # Temporary data storage\n    \n    def add_message(self, role, content):\n        """Add message to conversation history"""\n        message = {"role": role, "content": content, "timestamp": time.time()}\n        self.conversation_history.append(message)\n        \n        # Keep history within maximum length\n        if len(self.conversation_history) > self.max_turns:\n            self.conversation_history.pop(0)\n    \n    def get_context(self):\n        """Get conversation context (for sending to LLM)"""\n        return self.conversation_history[-self.max_turns:]\n    \n    def store_temp(self, key, value):\n        """Store temporary data"""\n        self.temporary_data = value\n    \n    def get_temp(self, key, default=None):\n        """Get temporary data"""\n        return self.temporary_data.get(key, default)\n    \n    def clear_temp(self):\n        """Clear temporary data"""\n        self.temporary_data.clear()\n

Long-Term Memory: Agent's Knowledge Base

Long-term memory is like the Agent's archive room, storing important information that needs to be retained long-term.

Features:

Large capacity: Can store a massive amount of information
Persistent: Information is saved long-term and will not be automatically cleared
Retrieval-based access: Retrieves relevant information through queries, rather than sequential reading
Structured storage: Information is usually stored in a structured way for easy retrieval

Main Uses:

Storing users' personal information and preferences
Accumulating knowledge and experience
Remembering important conversation content
Saving task execution results

Technical Implementation:

class LongTermMemory:\n    """Long-term memory base class"""\n    \n    def __init__(self):\n        self.memories = []  # Memory entry list\n    \n    def add_memory(self, content, metadata=None):\n        """Add memory"""\n        memory = {\n            "id": str(uuid.uuid4()),\n            "content": content,\n            "metadata": metadata or {},\n            "timestamp": time.time(),\n            "importance": 0.5  # Default importance\n        }\n        self.memories.append(memory)\n        return memory\n    \n    def search_memories(self, query, limit=5):\n        """Search memory (subclasses must implement specific search logic)"""\n        raise NotImplementedError\n    \n    def get_memory(self, memory_id\n

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