📚 LangGraph

Complete Master Guide

Class Notes & Reference — Banking, AI Agents & Production Systems

Section 1: What is LangGraph?

1.1Definition

LangGraph is a low-level orchestration framework for building stateful, multi-agent applications with Large Language Models (LLMs). It enables developers to create agent and multi-agent workflows with fine-grained control over both flow and state.

1.2Why LangGraph Exists — The Problem It Solves

Traditional LLM chains are stateless and linear. Real-world AI agents need:

Persistence — remember context across multiple steps and sessions

Branching — take different paths based on conditions (e.g. risk level)

Loops — retry, refine, or re-route until a goal is achieved

Parallelism — run independent tasks simultaneously to reduce latency

Human oversight — pause, await approval, then resume execution

Crash recovery — resume long-running workflows after failures

1.3Key Features

1.4Real-World Adoption

LangGraph is trusted in production by:

LinkedIn — candidate matching and recruiter assistance

Uber — internal operations automation

Klarna — customer service chatbots

GitLab — code review automation

J.P. Morgan — financial advisory workflows

Nubank — 120 million+ customer AI banking

Section 2: The Three Pillars of LangGraph

2.1State — The Shared Memory

State is the shared memory that flows through your entire application, carrying context from one step to the next. Think of it as the whiteboard that every worker (node) can read from and write to.

State Definition with Reducers

from typing_extensions import TypedDict

from typing import Annotated

from langgraph.graph.message import add_messages