A developer’s guide to agent memory

What you'll learn

• AI agents are evolving—from simple bots to sophisticated assistants that manage workflows, make decisions, and interact across tools. But while their responsibilities have grown, their ability to remember hasn’t kept up. Most agents still forget what was said moments ago, lose track of tasks mid-flow, and treat returning users like strangers.

  The missing link is memory.

  In this technical guide, you’ll explore why memory is essential to building intelligent, context-aware agents—and how to implement it effectively. You'll learn how modern memory architectures work, what types of memory agents need, and how to structure, retrieve, and maintain memory in production systems.

  Whether you're building a chatbot, research assistant, or autonomous workflow manager, this guide offers a clear, practical breakdown of how to make your agents smarter, faster, and more helpful—by helping them remember.

  5 things you’ll learn:

  • What is agent memory? Understand the difference between stateless tools and agents that learn, adapt, and improve over time.

  • Three types of memory that matter Learn how short-term, long-term, and structured memory work together to power real-world agent behavior.

  • How to design production-ready memory systems Get best practices for storing, tagging, retrieving, and updating memory across sessions and users.

  • How to balance context, cost, and performance Discover hybrid retrieval strategies and smart memory management techniques that reduce latency and LLM spend.

  • Code examples for real implementations See how memory works in practice with walkthroughs using LangGraph, vector search, and structured data storage.