Building LLM Agents with LangGraph

Build smart, adaptive LLM Agents with LangGraph. Handle multi-step tasks, maintain state, and compute solar panel energy savings for real-world AI applications.

In today's fast-evolving AI landscape, Retrieval-Augmented Generation (RAG) systems (LLM Agent scenarios: Agentic RAG) have become common for handling simple queries and generating context-aware responses.

However, with the growing demand for more complex AI applications, it is crucial to develop systems capable of handling multi-step tasks, maintaining state across interactions, and dynamically adapting to new information.

LangGraph, a powerful extension of the LangChain library, was created to address this challenge.

It enables the development of advanced LLM Agents with state management and looped computation, allowing developers to create smart, adaptive AI systems for real-world applications.

This article will explore how LangGraph is transforming AI development, using an example of an AI agent designed to calculate solar panel energy-saving potential, demonstrating its unique features and value.