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Description

Production-grade AI agent systems face two fundamental challenges: maintaining context across sessions and managing the computational costs of multi-step agentic workflows. This workshop addresses both through hands-on implementation of persistent memory systems and semantic caching.

We'll build agents with long-term memory using LangGraph's recently released 1.0 memory capabilities. You'll implement three memory types: semantic memory for storing structured facts and user preferences, episodic memory for retaining interaction history, and procedural memory for adapting agent behaviors over time. The workshop covers memory architecture patterns, checkpointing strategies, and cross-thread persistence using LangGraph's document store and LangMem SDK. Agent workflows generate significantly higher token usage than single-turn chat applications. To address this, we'll implement semantic caching using Redis LangCache.

Participants will leave with working code, architectural patterns for memory-enabled agents, and quantitative approaches to measuring cost optimization in production deployments.

Instructor's Bio

Benjamin Labaschin

Principal Machine Learning Engineer at Workhelix

Ben Labaschin is Principal MLE and founding engineer at Workhelix. He has worked as data scientist and economist in various places in the past decade.

Normie Tools for Validating LLM Outputs - As Principal MLE at a generative AI startup, I use LLMs a lot. Whether it be OpenAI or LLama 2, whatever the generative process is, the same problem persists: how can I trust the output generated for me is of the proper structure and content? It may surprise you to learn that more often than not, I will reach for the standard tools of the trade to accomplish 90% of my needs. In this talk I will discuss these tools and how you can use them to overcome the problem of stochastic generative output. 

Webinar

  • 1

    Workshop "Building Stateful AI Agents: Memory Management and Optimization with LangGraph and Redis"

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