Tired of AI agents with the memory of a goldfish? 🧠 We're revealing how to use n8n and Zep to build agents with a real, long-term memory using a relational knowledge graph, so they can finally understand your users.

We’ll talk about:

The complete guide to building n8n AI agents with a persistent, human-like memory using Zep's knowledge graph.
The "Token Problem"—how a great memory can lead to surprise five-figure bills, and the advanced architectures to cut costs by over 76%.
The Hybrid Memory Architecture: a "two-brain" approach combining Zep's knowledge graph for long-term understanding with a fast PostgreSQL database for short-term chat history.
How to use direct HTTP requests in n8n for a "surgical approach" to context filtering, dramatically reducing token usage.
A 4-week launch plan to take you from a basic concept to a fully deployed, intelligent agent with memory that scales to thousands of users.

Keywords: n8n, AI Agents, Long-Term Memory, Zep, Knowledge Graph, RAG, AI Memory, Vector Database, PostgreSQL, Pydantic AI, AI Automation, AI Workflow, AI Chatbot

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#71 Max: Unlock the Next Evolution of Agents with Human-like Memory (n8n + Zep)

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#71 Max: Unlock the Next Evolution of Agents with Human-like Memory (n8n + Zep)