LangGraph

Why It Matters

LangGraph addresses a fundamental limitation of simple agent loops: complex workflows need explicit control over state and execution flow. While basic agents work fine for straightforward tasks, production systems often require conditional branching, human-in-the-loop approval, parallel execution paths, and the ability to pause and resume workflows.

For AI engineers, LangGraph represents the evolution from “prompt and pray” to controlled, observable agent systems. Instead of hoping your agent makes the right decisions, you define explicit state machines that govern behavior. When something goes wrong, you can see exactly which node failed and why.

The graph-based approach also solves the persistence problem. Real applications need agents that can be interrupted, such as when a customer closes their browser, an approval takes days, or a rate limit forces a pause. LangGraph’s checkpointing lets you save and restore agent state, enabling workflows that span minutes, hours, or days.

LangGraph vs LangChain

LangChain and LangGraph serve different purposes, though they’re from the same team:

LangChain provides building blocks, including prompt templates, LLM integrations, document loaders, and vector store connections. It’s the toolkit you reach for when you need to make an LLM call, process documents, or connect to external services.

LangGraph orchestrates those building blocks into complex workflows. It manages state across multiple steps, controls execution flow through a graph structure, and handles persistence and checkpointing. You use LangChain components within LangGraph nodes.

Think of LangChain as the individual Lego bricks and LangGraph as the instruction manual for building something sophisticated. For simple chains (summarize this document, answer this question) LangChain alone is sufficient. For multi-step agents that need memory, branching logic, and fault tolerance, LangGraph provides the structure.

Implementation Basics

LangGraph workflows are built from three concepts:

State is a typed dictionary that flows through your graph. It holds everything the workflow needs to track, including user input, intermediate results, retrieved documents, and tool outputs. You define the state schema upfront, and LangGraph manages updates as execution proceeds.

Nodes are functions that receive state and return state updates. A node might call an LLM, execute a tool, format output, or make a routing decision. Each node has a single responsibility, so keep them focused and testable.

Edges control flow between nodes. Simple edges always go to the same next node. Conditional edges evaluate state and route to different nodes based on the result. This is where you implement branching logic: route to human review if confidence is low, or skip retrieval if the question is simple.

The typical pattern is: define your state schema, write node functions, connect them with edges (including conditional routing), compile the graph, then execute it with initial state. LangGraph handles the execution loop, state updates, and optional checkpointing.

Start with simple linear workflows before adding conditional edges. Get comfortable with state management and node composition. Add persistence once you need workflows that survive restarts or require human-in-the-loop approval.