Agentic Memory: Stateful RAG and AI Agents (Chapter 16)

The Memriq AI Inference Brief – Engineering Edition

Unlock the future of AI agents with agentic memory — a transformative approach that extends Retrieval-Augmented Generation (RAG) by incorporating persistent, evolving memories. In this episode, we explore how stateful intelligence turns stateless LLMs into adaptive, personalized agents capable of learning over time.

In this episode:

- Understand the CoALA framework dividing memory into episodic, semantic, procedural, and working types

- Explore key tools like Mem0, LangMem, Zep, Graphiti, LangChain, and Neo4j for implementing agentic memory

- Dive into practical architectural patterns, memory curation strategies, and trade-offs for real-world AI systems

- Hear from Keith Bourne, author of *Unlocking Data with Generative AI and RAG*, sharing insider insights and code lab highlights

- Discuss latency, accuracy improvements, and engineering challenges in scaling stateful AI agents

- Review real-world applications across finance, healthcare, education, and customer support

Key tools & technologies mentioned:

Mem0, LangMem, Zep, Graphiti, LangChain, Neo4j, Pinecone, Weaviate, Airflow, Temporal

Timestamps:

00:00 - Introduction & Episode Overview

02:15 - What is Agentic Memory and Why It Matters

06:10 - The CoALA Cognitive Architecture Explained

09:30 - Comparing Memory Implementations: Mem0, LangMem, Graphiti

13:00 - Deep Dive: Memory Curation and Background Pipelines

16:00 - Performance Metrics & Real-World Impact

18:30 - Challenges & Open Problems in Agentic Memory

20:00 - Closing Thoughts & Resources

Resources:

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

- Visit Memriq.ai for more AI engineering deep dives and resources