LangGraph OpenAI Serve

Serve LangGraph graphs through an OpenAI-compatible /v1 API so existing OpenAI SDKs, Chainlit, Open WebUI, and similar clients can call them without a project-specific protocol.

Install

Use uv for project dependency management:

uv add langgraph-openai-serve

The equivalent pip command is:

pip install langgraph-openai-serve

For deployments that use PostgreSQL for LangGraph checkpoints, Store data, or cross-worker interrupt coordination, install the optional integration:

uv add "langgraph-openai-serve[postgres]"

For polling-only background Responses executed by Hatchet, install its integration. InMemoryBackgroundBackend needs no extra for local trials; see Run Responses In The Background.

uv add "langgraph-openai-serve[hatchet]"

For built-in Langfuse tracing, install the tracing integration:

uv add "langgraph-openai-serve[tracing]"

Set LGOS_ENABLE_LANGFUSE=True together with LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY. LGOS creates the callback lazily on the first graph run; importing the package never initializes Langfuse.

The package contains the OpenAI-compatible server integration, not a built-in LLM graph. Applications register their own graphs. The demo/ checkout keeps each deployable application in an independent uv project with its own lockfile. Its lgos-rag example indexes a small corpus packaged with the demo API, so the entire directory can be copied and run without files from this repository.

Repository tasks require Bash and Just 1.58.0 or newer. Run just for package recipes and just demo/ for independent demo workflows. Use just --usage <recipe> to see a recipe's options and defaults.

Generate An Application

Create an independent project that lgos serve runs, with example graphs, tests, developer guidance, and Docker:

uvx cookiecutter https://github.com/ilkersigirci/langgraph-openai-serve.git \
  --directory lgos-starter-template

See the starter guide for setup, and Run The LGOS Server to serve your own registry.

Quick Demo

From this repository, prepare the demo environment and PostgreSQL:

cp demo/.env.example demo/.env
just demo/up lgos-db --wait
just demo/api --editable

Then call the demo with the OpenAI Python client:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:3004/v1", api_key="DUMMY")

response = client.responses.create(
    model="custom-input-output-context",
    input="Show me custom schemas.",
    store=False,
    user="demo-user",
)

print(response.output_text)

Existing Chat Completions clients can call the same simple graph through the same base URL:

completion = client.chat.completions.create(
    model="custom-input-output-context",
    messages=[{"role": "user", "content": "Show me custom schemas."}],
    user="demo-user",
)

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

Use Responses for new clients and advanced workflow features. The Chat example is the compatibility path for existing Chat-only clients.

Use curl http://localhost:3004/v1/models only as a diagnostic to inspect the registered demo graph names.

just demo/api --editable overlays this checkout without changing the self-contained demo project or its lockfile. The demo publishes independent API and Chainlit images and runs the pinned official Open WebUI image unchanged; its locked synchronization tool runs on the host. See the demo Docker Compose guide.

The complete Compose demo lets one OPENAI_GATEWAY_TYPE=litellm|bifrost setting place either gateway in front of both maintained UI clients. Chainlit and Open WebUI use normal managed/native Responses and Files routes. Metadata comes from LiteLLM's native /model/info after model sync, or Bifrost's native /v1/models after catalog sync. Neither UI connects directly to LGOS. The PostgreSQL-persistent Chainlit client uses a shared mock login by default, with OIDC login available as an opt-in mode. See the Chainlit demo.

Use In FastAPI

from fastapi import FastAPI
from langgraph_openai_serve import GraphConfig, GraphRegistry, LanggraphOpenaiServe
from your_graphs import my_graph

app = FastAPI()
registry = GraphRegistry(
    graphs={
        "my-graph": GraphConfig(
            graph=my_graph,
            description="Answer questions with my LangGraph workflow.",
        )
    }
)

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

The default base URL is {host}/v1. Registered graph names become OpenAI model values.

LGOS accepts native Responses input_file parts with opaque file_id values, but does not own file upload or storage. Deploy one Files API for the graph services that share a file namespace, or use a gateway-native Files provider. The standalone S3-backed demo Files API is a small reference deployment. Chat Completions remains available for direct compatibility clients; the maintained demo UIs use Responses exclusively. See Choose Responses or Chat Completions for a feature-by-feature comparison.

Docs

• Documentation home: docs/index.md
• Package getting started: docs/getting-started.md
• Architecture and state ownership: docs/explanation/architecture.md
• Self-contained demo stack: docs/demo/index.md
• Self-hosted service references: docs/demo/self-hosted.md
• Runnable demo API: docs/demo/api.md
• Demo graph catalog: docs/demo/graphs/
• OpenAI clients: docs/tutorials/openai-clients.md
• Custom graphs: docs/tutorials/custom-graphs.md
• LangGraph runtime settings: docs/how-to-guides/langgraph-runtime-settings.md
• Background Responses: docs/how-to-guides/background-responses.md
• OpenAI-compatible proxies: docs/how-to-guides/openai-proxies.md
• API and configuration: docs/reference.md
• Compatibility contract: docs/explanation/openai-compatibility.md