NeuralPath — AI Engineer Mastery Terminal

LangGraph models an agent as a state graph — explicit nodes (functions), edges (transitions), and a shared state object passed between them. It's the deterministic-control answer to "agents are too unreliable".

Core concepts

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

class State(TypedDict):
    query: str
    docs: List[str]
    answer: str
    iters: int

def retrieve(state): ...
def grade(state): ...
def rewrite(state): ...
def generate(state): ...

g = StateGraph(State)
g.add_node("retrieve", retrieve)
g.add_node("grade", grade)
g.add_node("rewrite", rewrite)
g.add_node("generate", generate)

g.set_entry_point("retrieve")
g.add_edge("retrieve", "grade")
g.add_conditional_edges(
    "grade",
    lambda s: "rewrite" if not s["docs"] and s["iters"] < 3 else "generate",
    {"rewrite": "rewrite", "generate": "generate"},
)
g.add_edge("rewrite", "retrieve")
g.add_edge("generate", END)

app = g.compile()

Why this beats raw ReAct in production

Explicit termination. No "the model decides when to stop" — you write the conditional that ends the loop.
Resumability. State is serializable; a graph can be paused and resumed (e.g., human approval mid-run).
Observability. Every node's input/output is naturally traceable.
Composability. Subgraphs become reusable — a "retrieve+grade" subgraph plugs into bigger workflows.
Determinism where you want it. Some nodes are pure functions; only LLM-decision nodes are non-deterministic.

Patterns that drop out naturally

Loop-with-grader (CRAG): retrieve → grade → rewrite/generate.
Plan-execute: planner node emits a plan, executor node runs each step.
Reflect: generate → reflect → revise.
Branching: classifier node routes to specialist subgraphs.
Human-in-the-loop: pause node waits for human input via webhook or UI.

Concrete pitfalls

State mutation — pass clean diffs; mutating shared state across nodes hides bugs.
Unbounded loops — always include an iters field and cap it in your conditional.
Massive state objects — keep state lean; persist intermediate large blobs externally.
Logging drift — log node inputs/outputs to LangSmith / LangFuse for trajectory eval.

When NOT LangGraph

The workflow is a straight pipeline → LangChain LCEL or even raw functions.
You need fine-grained streaming guarantees per token → use raw API + your own router.
Your team doesn't run Python; LangGraph.js is younger and rougher.

Comparable alternatives