LangGraph Functional API Overview

Learn how to leverage LangGraph's new Functional API to build AI workflow agents with minimal code changes. In this tutorial, we transform an agent implemented in Python into a LangGraph-agent using a few simple decorators. We clearly show what LangGraph enables, including persistence for short-term memory, human-in-the-loop, streaming, tracing / debugging, and long-term memory. Docs: https://langchain-ai.github.io/langgraph/concepts/functional_api/ Video Chapters: 00:00 Introduction to LangGraph Benefits 01:00 Building a Vanilla Python Agent 02:45 Introducing LangGraph's Functional API 07:40 Adding Human-in-the-Loop Capabilities 11:30 Time Travel and State Management 14:00 Implementing Long-Term Memory 18:00 Conclusion and Benefits Review Video Notes (notebook referenced): https://github.com/langchain-ai/langgraph/blob/6ed63ba8fc73fc1e0d205b1353d434c013defae8/docs/docs/tutorials/functional_api/react_functional_api.ipynb