Memory Management

Memory management provides agents with a “brain” that persists beyond the current context window. It involves storing user preferences, conversation history, and factual knowledge in a database (like a Vector DB or SQL) and retrieving relevant information when needed. Without memory, an agent is amnesic, resetting after every session.

When to Use

• Personalization: Remembering user names, preferences, and past choices.
• Long-Running Tasks: Tracking progress on a project that spans days or weeks.
• Context Awareness: Understanding references to previous conversations (“As I mentioned earlier…”).
• Learning: Improving performance by recalling past mistakes or feedback.

Use Cases

• Chatbots: Maintaining conversation history for context (Short-term memory).
• User Profiles: Storing “User is a vegetarian” in a profile database (Long-term memory).
• Knowledge Base: Accumulating facts learned from searching the web (Episodic memory).

Implementation Pattern

class Memory:
    def add(self, content):
        # Store in Vector DB or SQL
        pass
        
    def retrieve(self, query):
        # Search for relevant memories
        pass

def memory_augmented_agent(user_input, user_id):
    # Step 1: Recall
    # Retrieve relevant history and user facts
    context = memory.retrieve(query=user_input, tags=[user_id])
    
    # Step 2: Augment Prompt
    prompt = f"""
    Context from memory: {context}
    User Input: {user_input}
    Answer the user, taking into account their history.
    """
    
    # Step 3: Generate
    response = llm.generate(prompt)
    
    # Step 4: Memorize
    # Store the new interaction
    memory.add(f"User: {user_input} | Agent: {response}")
    
    return response