Explore MemGPT, an innovative approach treating Large Language Models (LLMs) as operating systems. This article explains how MemGPT overcomes LLM long-term memory and context window limitations through its core 'Prompt Compilation' technique and unique memory management mechanisms (Core Memory, Recall Memory, Archival Memory) to enable more persistent conversational interactions.

Explore the paper 'Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory.' Learn how Mem0 addresses long-term memory challenges in LLMs, detailing its core architectures (Mem0 & Mem0^g), memory management techniques, and experimental results on the LOCOMO dataset.

Explore the PLAN-AND-ACT paper: Learn how its innovative Planner-Executor framework and data synthesis methods enhance Large Language Model (LLM) capabilities in long-horizon task planning and execution, and overcome related challenges.

Explore the core concepts of Agent Memory (Semantic, Episodic, Procedural) for LLMs. Learn practical implementation of long-term agent memory using LangGraph & LangMem, based on DeepLearning.AI course notes, with a detailed Email Agent example.

Understand how to train an o1-like model on specific domain

Dive into the HuggingGPT paper, a key work on LLM Agents. Discover how an LLM acts as a controller for task planning and tool usage to solve multi-modal and complex AI challenges.

LLM Agent for Multi-Table QA Task