The LangChain Agent Stack Explained (Tools, Memory, RAG & Planning)
Автор: AI Learning Hub - Byte-Size AI Learn
Загружено: 2026-01-08
Просмотров: 99
Описание:
LangChain agents power some of the most advanced AI applications today — from autonomous task execution to tool-using LLMs and multi-step reasoning systems.
In this video, we break down the LangChain Agent Stack and explain how each layer works together to build reliable, production-ready AI agents.
You’ll learn:
What makes an AI agent different from a chatbot
The core components of the LangChain agent stack
How tools, memory, planning, and retrieval fit together
How LangChain agents execute actions and reason step by step
Best practices for building scalable agent-based AI systems
This video is ideal for AI engineers, backend developers, and anyone building AI agents using LangChain, vector databases, and RAG pipelines.
📌 Topics covered:
LangChain agents architecture
Tool calling and function execution
Agent memory and state
Planning and reasoning loops
RAG + agents integration
👉 Subscribe for more deep dives on AI agents, LangChain, and modern LLM system design.#ai #ArtificialIntelligence #MachineLearning #AITools #AIUpdates #ChatGPT #OpenAI #TechNews #AITrends2025 #FutureOfAI
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