LangGraph Multi-Agent Systems: Build Autonomous AI Workflows

Why LangGraph?

LangChain agents are powerful but struggle with complex, branching workflows. LangGraph solves this by modelling your agent as a directed graph with explicit state transitions.

Core Concepts

A LangGraph application has three building blocks: State (a typed dict), Nodes (functions that update state), and Edges (routing logic).

Building a Research Agent

We build a research agent that can search the web, summarise results, decide if it needs more information, and compile a final structured report — all with persistent memory between steps.

Tool Calling and Human-in-the-Loop

LangGraph makes it trivial to add tool nodes and checkpoint the graph state for human review before proceeding — critical for production AI systems.

Deploying with FastAPI

Wrap the graph in a FastAPI endpoint with streaming support so the frontend can show intermediate agent thoughts in real time.