Context engineering & agent memory with LangGraph and Redis

AI agents are evolving fast—from simple chatbots to highly capable tools that perform multi-step reasoning and handle complex workflows. But one of the biggest hurdles devs face is memory. Without robust memory, agents forget prior steps, hallucinate responses, and rack up API costs by repeatedly asking the same questions.

This guide explores how to solve those problems using LangGraph—a library from LangChain for building stateful, multi-step agent workflows—and Redis—an ultra-fast database ideal for caching, vector search, and managing persistent state.

Whether you're building a retrieval-augmented generation (RAG) system, a customer support agent, or a multi-agent orchestration layer, mastering context engineering and agent memory is key to stability, performance, and scalability.

Download the guide to get example architectures, practical advice, and a deep dive into building scalable AI apps.