Introducing Agentic AI: OctoTools! Understand how OctoTools enhances the performance of LLM Agents on complex tasks through its well-defined and extensible Tool Cards, and the ingenious interplay between its Planner and Executor.

Explore an ICLR 2025 paper on guiding Large Language Models (LLMs) between code execution and textual reasoning. Learn why models like GPT-4o may prefer text-based approaches, sometimes leading to errors, and how combining both reasoning methods yields the best performance.

Discover Pre-Act, an approach enhancing Large Language Model (LLM) agent performance through multi-step planning and reasoning. Learn how Pre-Act overcomes ReAct's limitations in long-term planning by generating and modifying plans at each thinking step, thereby improving acting capabilities.

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.