Build Production RAG with Agentic Memory - Agent SDK + MCP (Free Template)

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A video overview demonstrating Graphiti’s capabilities and how it models dynamic, evolving relationships for AI agents with temporal knowledge graphs.

About Me 🙋‍♂️
I'm Imtiaz, bringing AI Data Science & Engineering expertise from Fortune 500 companies to my AI Agency. I focus on building custom solutions that deliver measurable business value by transforming innovative ideas into practical implementations.

📚 Resources

Graphiti GitHub Repository (Official)
https://github.com/getzep/graphiti GitHub — The open-source framework for building temporally-aware knowledge graphs for AI agents
Zep Research Paper
https://arxiv.org/abs/2501.13956 arXiv — "Zep: A Temporal Knowledge Graph Architecture for Agent Memory"
Graphiti Documentation
https://help.getzep.com/graphiti/gett... Zep — Official docs for getting started with Graphiti


📑 Chapters
0:00 — Introduction & What You'll Build
1:01 — The Problem: Why Most AI Agents Have Zero Memory
1:23 — Solution Overview: Self-Evolving Memory Agent
2:06 — Why This Matters for AI Agencies & Technical Founders
3:43 — Knowledge Graphs vs Vector Databases
5:02 — Agent Builder Setup: Prompt, LLM & Tools
6:38 — MCP Server Explained: Connecting External Databases
7:07 — Custom Graphiti MCP Server Setup in Replit
9:58 — Connecting to Neo4j Cloud Database
11:36 — Demo: Storing Facts in the Knowledge Graph
13:36 — Demo: Invalidating Old Facts (Temporal Updates)
15:42 — Publishing Your Agent to Production
16:19 — File Search vs Knowledge Search Comparison
17:22 — Traditional RAG vs Graph RAG vs Temporal Graphs
19:19 — Retrieval Speed: Temporal vs Graph vs Vector
20:07 — Agent SDK Tutorial (Google Colab Walkthrough)
22:11 — Defining Tools with the @tool Decorator
23:03 — Testing the Memory Agent: State Changes Over Time
25:28 — Launching the Gradio Chat Interface
26:51 — Conclusion: What Separates Production Engineers from "Vibe Bros"

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