Procedural Memory for RAG: Deep Dive with LangMem (Chapter 18)
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Procedural Memory for RAG: Deep Dive with LangMem (Chapter 18)
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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