Mem0: Building AI Agents with Scalable Long-Term Memory

Taranjeet Singh, Co-Founder & CEO of Mem0, breaks down the critical importance of memory in AI agents. Learn about the limitations of current agent architectures, common misconceptions around memory systems, why you need a standalone layer (many systems use multiple LLMs), and unveils Mem0's approach to scalable, contextual, and dynamic memory for agentic frameworks. Essential viewing for developers building production-ready AI agents.

Chapters:
00:00 Intro – The AI Memory Layer

01:15 Why AI Agents Need Long-Term Memory

02:00 Why Vector Databases and RAG Are NOT True AI Memory

02:56 What Makes Agent Memory Different from Storage

03:42 How Mem0 Adds Memory to AI Agents in 3 Lines of Code

04:57 Behind the Scenes: Mem0's AI Memory Architecture

05:30 Using Graph Memory for Complex Agent Relationships

06:10 AI Memory Benchmark: Mem0 vs Vector DBs vs Full Context

06:42 Where Agent Memory Matters: Real-World Use Cases

07:19 Mem0 in Action: Browserbase and Revision Dojo Case Studies

08:20 Vision: A Universal Memory Passport for AI

08:55 Meet the Team Behind Mem0’s AI Memory System

09:14 3 Key Takeaways About AI Agent Memory

09:42 Q&A – Memory Architecture, Stale Memory & More

Learn more about Mem0: https://mem0.ai/
Learn more about AI memory: https://arize.com/ai-memory/

Видео Mem0: Building AI Agents with Scalable Long-Term Memory канала Arize AI