Unlock the power of procedural memory to transform your Retrieval-Augmented Generation (RAG) agents into autonomous learners. In this episode, we explore how LangMem leverages hierarchical learning scopes to enable AI agents that continuously adapt and improve from their interactions — cutting down manual tuning and boosting real-world performance.

In this episode:

- Why procedural memory is a game changer for RAG systems and the challenges it addresses

- How LangMem integrates with LangChain and OpenAI GPT-4.1-mini to implement procedural memory

- The architecture patterns behind hierarchical namespaces and momentum-based feedback loops

- Trade-offs between traditional RAG and LangMem’s procedural memory approach

- Real-world applications across finance, healthcare, education, and customer service

- Practical engineering tips, monitoring best practices, and open problems in procedural memory

Key tools & technologies mentioned:

- LangMem

- LangChain

- Pydantic

- OpenAI GPT-4.1-mini

Timestamps:

0:00 - Introduction & overview

2:30 - Why procedural memory matters now

5:15 - Core concepts & hierarchical learning scopes

8:45 - LangMem architecture & domain interface

12:00 - Trade-offs: Traditional RAG vs LangMem

14:30 - Real-world use cases & impact

17:00 - Engineering best practices & pitfalls

19:30 - Open challenges & future outlook

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Memriq AI: https://memriq.ai