LangGraph Has a Free API: Build Stateful AI Agents That Actually Work

Most AI agent frameworks are demos that break in production. LangGraph is designed for agents that need to actually work.

What Is LangGraph?

LangGraph builds stateful, multi-step AI agents as graphs. From LangChain team: state management, cycles (agents loop and self-correct), human-in-the-loop, streaming, and checkpointing.

from langgraph.graph import StateGraph, START, END
from langchain_openai import ChatOpenAI

class State(TypedDict):
    messages: list
    next_step: str

def researcher(state):
    response = llm.invoke(state["messages"] + [{"role": "system", "content": "Research thoroughly."}])
    return {"messages": state["messages"] + [response], "next_step": "writer"}

graph = StateGraph(State)
graph.add_node("researcher", researcher)
graph.add_node("writer", writer)
graph.add_edge(START, "researcher")
graph.add_edge("writer", END)
app = graph.compile(checkpointer=MemorySaver())

Why LangGraph wins: deterministic control flow, persistent memory across runs, human approval before risky actions, multi-agent orchestration. If your agent needs to think, loop, and adapt — use LangGraph.

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