Unlock the Next Evolution of Agents with Human-like Memory (n8n + zep)

Video Description

🌟 Want my full course on building AI Agents with no code?👇 https://www.skool.com/ai-automation-society-plus/about 📌 Join my FREE Skool community for all the resources to set this system up! 👇 https://www.skool.com/ai-automation-society/about 🚧 Start Building with n8n! (I get kickback if you sign up here - thank you!) https://n8n.partnerlinks.io/22crlu8afq5r 📚 Try Out Zep: https://www.getzep.com/ In this video, I walk you through how to use Zep memory to give your AI agents true long-term memory, not just basic context retention, but memory stored in a relational graph. This allows your agents to understand entities, relationships, and even connections between different users, making them far more intelligent and context-aware. But while this unlocks the next evolution of AI agents, blindly adding Zep memory can be a dangerous game, especially as you scale, with costs quickly adding up. So in this tutorial, I break down how to effectively combine short-term and long-term memory, and I share several smart strategies to reduce the cost of running these systems without sacrificing performance. Sponsorship Inquiries: 📧 [email protected] WATCH NEXT: https://youtu.be/Ik8OHT3w4pE?si=58-J3hJecjpZ80if TIMESTAMPS 00:00 Short Term vs. Long Term Memory 02:36 Zep Relationship Graph 05:47 The Issue with This Approach 07:07 Method 1 for Token Reduction 11:37 Download the FREE Workflow 11:57 Method 2 for Token Reduction 13:57 What is a Session ID? 15:32 How to Master n8n Gear I Used: Camera: Razer Kiyo Pro Microphone: Blue Yeti USB