Building Multi-Agent Systems with LangGraph

Single-prompt LLM apps hit a ceiling fast. The moment you need a model to retrieve context, call a tool, reflect on the result, and maybe try again, a one-shot prompt turns into a tangle of instructions that nobody can reason about. This is where agent orchestration earns its keep.

Why a graph?

LangGraph models your application as a state machine: nodes do work, edges decide where to go next, and a shared state object flows through the whole thing. That structure buys you three things:

• Explicit control flow - you can see exactly how the agent moves between retrieval, generation, and tool calls.
• Loops that terminate - reflection and retry become first-class edges with clear exit conditions, instead of hidden while loops.
• Inspectable state - every transition operates on a typed state object, so debugging is reading data, not guessing.

A minimal shape

Here’s the skeleton of a retrieval-then-answer graph. The details vary, but the shape is almost always the same:

from langgraph.graph import StateGraph, END
from typing import TypedDict

class State(TypedDict):
    question: str
    context: list[str]
    answer: str

def retrieve(state: State) -> State:
    state["context"] = search(state["question"])
    return state

def generate(state: State) -> State:
    state["answer"] = llm(state["question"], state["context"])
    return state

graph = StateGraph(State)
graph.add_node("retrieve", retrieve)
graph.add_node("generate", generate)
graph.set_entry_point("retrieve")
graph.add_edge("retrieve", "generate")
graph.add_edge("generate", END)

app = graph.compile()

Adding a reflection loop

The interesting part is conditional edges. Say you want the agent to grade its own answer and retry retrieval if the answer is weak:

def should_retry(state: State) -> str:
    return "retrieve" if grade(state["answer"]) < 0.7 else "done"

graph.add_conditional_edges(
    "generate",
    should_retry,
    {"retrieve": "retrieve", "done": END},
)

That single conditional edge is the whole reflection pattern. No hidden loops, no runaway recursion - just a graph you can draw on a whiteboard.

What I’d keep in mind

A few things that have saved me pain:

1. Keep state small and typed. If your state object grows unbounded, so does your token bill and your confusion.
2. Cap your loops. Always add a max-iterations guard so a stubborn grader can’t spin forever.
3. Log every transition. The graph structure makes this easy, and it turns “why did the agent do that?” into a five-minute answer.

Graph-based orchestration won’t make a bad model good, but it will make a capable model predictable - and in production, predictable is most of the battle.