Context engineering & agent memory with LangGraph and Redis

What you'll learn

• AI agents are evolving fast—from simple chatbots to stateful systems that reason across steps, use tools, and operate continuously in production. But most agents fail for the same reason: they don’t have access to the right context at the right time.

  Without a robust approach to context and memory, agents lose state between steps, hallucinate responses, repeat work, and behave unpredictably as complexity grows.

  This guide shows how to solve those problems using LangGraph, a framework from LangChain for orchestrating stateful, multi-step agent workflows, and Redis, the real-time context engine that gathers, stores, and serves the memory, state, and knowledge agents need to behave accurately and reliably.

  Whether you’re building a RAG-powered apps, a customer support agent, or a multi-agent system, mastering context engineering and agent memory is fundamental to building AI apps that are fast, accurate, and scalable in the real world.

  Download the guide to explore reference architectures, practical implementation patterns, and proven techniques for taking AI agents from prototype to production.