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Agentic Long-Term Memory for LLMs — Why You Shouldn’t Rely on LangChain or Letta 11 дней назад


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Agentic Long-Term Memory for LLMs — Why You Shouldn’t Rely on LangChain or Letta

In this video, we dive deep into agentic long-term memory for LLM-based applications. We’ll start from the basics and work our way up to designing advanced memory systems. You'll learn how to build a custom chatbot with agentic memory from scratch, and explore two leading frameworks in this space: Letta and LangChain. 🚀 Full code and materials: https://github.com/Farzad-R/Agentic-L... 🔗 Connect on LinkedIn:   / farzad-roozitalab   🎥 Related series: • Advanced Q&A and RAG series: https://github.com/Farzad-R/Advanced-... • LLM-Zero-To-Hundred Series: https://github.com/Farzad-R/LLM-Zero-... 🛠 Frameworks and Tools Used: #openai #langchain #letta #gradio #sqlite #chatbot #rag #llm #agent #python #gpt #memory 📚 Timestamps: 00:00:00 Intro 00:07:21 Importance of Memory 00:14:12 What is Memory? 00:30:43 Custom Chatbot with Basic Memory 00:58:50 Custom Chatbot with Agentic Memory (V1) 01:33:38 Custom Chatbot with Agentic Memory (V2) 01:45:33 Letta 01:48:55 LangChain's First Long-Term Memory Strategy (Vector Database + Knowledge Graph) 02:10:10 LangChain's New Memory Architecture (Semantic, Episodic, and Procedural Memory)

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