AI Memory Systems

Introduction

AI memory lets a chatbot remember past conversations across sessions. It's RAG over your own conversation history — store summaries and facts, retrieve them when relevant.

Beginner analogy: Like the assistant in a coffee shop who remembers your usual order.

Understanding the topic

Core concepts to understand:

• Short-term memory = current conversation history.
• Long-term memory = facts extracted from past sessions, stored in a vector DB.
• Extract memories with an LLM after each session.
• Retrieve relevant memories at the start of new sessions.
• Tools: mem0, Letta (formerly MemGPT), Zep.

Syntax reference

Visual workflow / architecture:

End of session:
   conversation → LLM → extract facts → store in memory DB

Start of next session:
   user msg → search memory DB → inject relevant facts → LLM

Real-world use

ChatGPT's 'memory' feature, Replika, Pi (Inflection) all use memory layers like this.

Best practices

• Always let users see and delete their memories.
• Periodically prune stale memories.
• Encrypt memory at rest — it's PII.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. How does long-term memory work in chatbots?
• Q2. Tools for AI memory?
• Q3. Privacy considerations?