Building LLM Agents with LangGraph #1: Introduction to LLM Agents & LangGraph
Author(s): Youssef Hosni
Originally published on Towards AI.
Step-by-Step Guide to Building LLM Agents with LangGraph
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LLM agents are a very powerful tool for automating complex workflows. In this first article of the Building LLM Agents with LangGraph series, we lay the foundation for understanding LLM agents and their agentic workflows.
We explore what LLM agents are, the fundamental building blocks that make them work, and the concept of agentic workflows. By diving into key design patterns, such as self-reflection, tool use, planning, and multi-agent collaboration, readers will gain insights into how these patterns enhance agent capabilities.
The article also introduces LangGraph, a framework designed to streamline the creation and management of LLM agents, highlighting its core concepts and advantages. This introduction serves as the gateway to mastering the principles and practicalities of building LLM agents with LangGraph, which we will build in the coming articles.
This article is the first article in the ongoing series of Building LLM Agents with LangGraph:
Introduction to Agents & LangGraph (You are here!)Building Simple ReAct Agent from Scratch (Coming Soon!)Main Building Units of LangGraph (Coming Soon!)Agentic Search Tools in LangGraph (Coming Soon!)Persistence and Streaming in LangGraph (Coming Soon!)Human in the Loop in LLM Agents (Coming Soon!)Putting it All Together! Building Essay Writer… Read the full blog for free on Medium.
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