Hands-on : Build Agentic Workflow using LangGraph (Key Learnings from Langchain-academy) | Part-1 | by Anurag Mishra | Medium

Langchain has recently introduced an impressive course focusing on LangGraph and its key features for developing robust agentic and multi-agentic workflows. In this series, we will explore essential insights from the course and create applications utilizing agentic workflows. In the first part, we’ll cover fundamental concepts of LangGraph and how to get started. The second part will focus on developing a comprehensive end-to-end agent-based application using LangGraph. In most LLM-based applications, tasks are typically organized in a sequential manner, known as ‘chains.’ While this approach ensures reliability and consistent execution, it can be somewhat rigid. What if we allowed LLMs to determine the next action? An agentic workflow offers an iterative and collaborative model, transforming interactions with LLMs into a series of manageable, refinable steps. This approach enables continuous improvement and adaptation throughout the task completion process. A system is more “agentic”

saved by

• Leonardo