Agent Memory Guide
A comprehensive guide to building effective memory systems for AI agents.
Why Agent Memory Matters
Agent memory is a response to concrete engineering problems:
- How can an agent maintain consistent behavior across long time horizons, when the underlying model is stateless and the context window is finite?
- How can an agent reduce repeated failures without retraining the model for every new environment?
- How can agents support richer forms of personalization than “retrieve a few relevant past messages”?
- How can teams move from prompt‑only tuning to systems that automatically improve through memory of past experiences?
Throughout this guide, we’ll consider two types of AI agents that AI engineers commonly work with:
Conversational AIs - Chatbots where an end user can exchange messages with your AI agent.
Autonomous Agents - Agents that are intended to automate specific tasks and potentially entire business functions.
What You’ll Learn
- Fundamentals: Understanding the core principles of agent memory and why it’s challenging
- Core Concepts: Deep dive into context engineering, retrieval patterns, and state management
- Patterns & Architectures: Proven approaches from sliding windows to graph-based memory
- Implementation: Hands-on guidance for building memory systems in production
- Advanced Topics: Security, compliance, scaling, and optimization strategies
- Benchmarks: Real-world comparisons and when not to use memory
Quick Start
- Start with What is Agent Memory? to understand the fundamentals
- Learn about The Memory Problem and why it’s technically challenging
- Explore Patterns & Architectures to find the right approach for your use case
- Follow the Implementation Guide to build your first memory system
Who This Guide Is For
- AI Engineers building conversational agents and assistants
- Product Managers designing agent experiences
- Researchers exploring memory architectures
- Developers integrating memory into existing systems
This guide is maintained by the AI community. Contribute on GitHub .