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Get Started With The Package

Build a small application that registers one LangGraph graph as an OpenAI model using the installed langgraph-openai-serve package.

Looking for a ready-made stack?

The self-contained demo provides example graphs, PostgreSQL, Docker Compose, Chainlit, Open WebUI, and Bifrost.

Install

Create or enter a Python 3.11 or newer project, then add LGOS:

uv add langgraph-openai-serve

pip install langgraph-openai-serve

LGOS installs its FastAPI, LangGraph, OpenAI SDK, and server dependencies. Add the model providers, tools, and persistence packages required by your graphs.

Create A Graph And Application

Create app.py:

app.py
from fastapi import FastAPI
from langchain_core.messages import AIMessage
from langgraph.graph import END, START, MessagesState, StateGraph

from langgraph_openai_serve import (
    GraphConfig,
    GraphRegistry,
    LanggraphOpenaiServe,
)


def respond(state: MessagesState) -> dict[str, list[AIMessage]]:
    text = str(state["messages"][-1].content)
    return {"messages": [AIMessage(content=f"LGOS received: {text}")]}


graph = (
    StateGraph(MessagesState)
    .add_node("respond", respond)
    .add_edge(START, "respond")
    .add_edge("respond", END)
    .compile()
)

registry = GraphRegistry(
    registry={
        "echo": GraphConfig(
            graph=graph,
            description="Echo the latest user message.",
        )
    },
)

app = FastAPI()
LanggraphOpenaiServe(app=app, graphs=registry).bind_openai_api()

The registry key echo is the OpenAI model name. Its required description is advertised by the LGOS model extension for clients that render model catalogs. This deterministic graph is deliberately provider-free, so the first request needs no upstream API key.

Run The Server

uvicorn app:app --reload

The OpenAI-compatible base URL is http://localhost:8000/v1.

Call The Graph

Use the ordinary OpenAI Python client installed with LGOS:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="DUMMY")

response = client.chat.completions.create(
    model="echo",
    messages=[{"role": "user", "content": "Hello from an OpenAI client"}],
)

print(response.choices[0].message.content)

The result is LGOS received: Hello from an OpenAI client in a standard Chat Completions response. The dummy key satisfies the SDK; LGOS does not enforce authentication unless the host application adds it.

Next Steps