LangGraph Tutorial: Build Multi-Agent AI Systems Step by Step

Single-prompt AI bots are a relic of 2023. In 2026, enterprise companies and top-tier startups require Multi-Agent Systems—architectures where specialized AI workers collaborate autonomously to solve complex workflows.

LangGraph, developed by the creators of LangChain, is the premier framework for building robust, stateful multi-agent applications using cyclical graph structures.

The Supervisor + Worker Architecture

In this tutorial, we will build a production-grade research team consisting of:

1. Supervisor Router Agent: Directs incoming tasks to the correct worker node.
2. Web Scraper Agent: Extracts clean markdown from target URLs using Exa.ai.
3. Data Synthesizer Agent: Formats scraped data into structured JSON schemas.

                 ┌────────────────────────────────────────┐
                 │       USER RESEARCH QUERY INPUT        │
                 └───────────────────┬────────────────────┘
                                     ▼
                 ┌────────────────────────────────────────┐
                 │        SUPERVISOR ROUTER NODE          │
                 └────┬──────────────────────────────┬────┘
                      │                              │
           ┌──────────┴──────────┐        ┌──────────┴──────────┐
           ▼                     ▼        ▼                     ▼
  ┌──────────────────┐                  ┌──────────────────┐
  │ WEB SCRAPER NODE │                  │ DATA SYNTHESIZER │
  └────────┬─────────┘                  └────────┬─────────┘
           └─────────────────► ◄─────────────────┘

Step 1: Defining the LangGraph State Graph

In LangGraph, all agents communicate by modifying a central State object.

main.pyRunnable
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from typing import Annotated, Sequence, TypedDict
from langchain_core.messages import BaseMessage
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]
    next_node: str
    extracted_data: dict

Step 2: Wiring the Supervisor Node

The Supervisor uses Claude 3.5 Sonnet to determine which worker node should execute next.

main.pyRunnable
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def supervisor_node(state: AgentState):
    # LLM reasoning logic to evaluate state.messages
    # Returns the target node: "WebScraper" or "Synthesizer"
    pass

workflow = StateGraph(AgentState)
workflow.add_node("Supervisor", supervisor_node)
workflow.add_node("WebScraper", scraper_node)
workflow.add_node("Synthesizer", synth_node)

## Define conditional edges
workflow.add_conditional_edges("Supervisor", lambda x: x["next_node"])
workflow.set_entry_point("Supervisor")

app = workflow.compile()

Why LangGraph is Superior to Traditional Code

By structuring your AI workflows as state graphs, you gain automatic persistence (memory), error recovery loops, and deterministic human-in-the-loop approval triggers.

👉 Ready to deploy enterprise LangGraph agents? Dive into Phase 4 of PyNex Vibe Coding!