Sub-25ms Decision Nodes for LangGraph Agents

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

# Initialize model through JevProxy Reflex Gateway
fast_router = ChatOpenAI(
    base_url="https://api.jevproxy.com/v1",
    api_key=os.environ.get("JEVPROXY_API_KEY"),
    model="gpt-4o",  # Transparently accelerated with 18ms JEV reflexes
    temperature=0
)

# Define conditional edge function
def route_step(state):
    # Routine deterministic decisions resolve in 18.4ms instead of 1,420ms
    response = fast_router.invoke(f"Route next step for: {state['task']}")
    return "execute_tool" if "tool" in response.content else "summarize"

# Build your LangGraph state graph
workflow = StateGraph(dict)
workflow.add_node("agent", lambda state: state)
workflow.add_conditional_edges("agent", route_step, {
    "execute_tool": "agent",
    "summarize": END
})