What is Agent Memory?

Introduction

Agent memory is the system that allows AI agents to retain, recall, and utilize information from past interactions to inform future responses and decisions.

Early memory systems were simple RAG-based search solutions that allowed chatbots to remember information across user sessions.

More modern agent memory systems are focused on agent learning as well.

Key Characteristics

Persistence

Information survives beyond single conversation turns
Data maintained across sessions and interactions
Long-term storage of important context

Retrieval

Ability to surface relevant past information
Context-aware memory access
Efficient search and filtering

Learning

Adaptation based on interaction patterns
Preference learning and personalization
Continuous improvement over time

Types of Memory

Working Memory

Short-term context within the current conversation or task.

Episodic Memory

Specific events, conversations, and interactions with detailed context.

Semantic Memory

General knowledge, facts, and learned patterns about users, domains, and preferences.

Why Memory is Critical

Without memory, agents:

Lose context between conversations
Cannot personalize experiences
Repeat failed strategies
Cannot build on previous successes
Provide inconsistent responses