What this tutorial covers

A client may expect an OpenAI-shaped Chat Completions request and response, while the application behind the endpoint is a LangGraph workflow. Those are different concerns. The request and response format belongs to the public interface; state, routing, tools, model calls, streaming, persistence, and human-in-the-loop execution belong to the workflow implementation.

This separation is the central design point for a LangGraph service exposed through FastAPI. A compatibility adapter receives and validates the public request, invokes the graph using an application-owned representation, and assembles a response in the format expected by the client. If streaming is offered, a separate SSE serializer must emit events with the intended semantics. Keeping those responsibilities distinct makes it possible to reason about each boundary instead of assuming that a graph’s internal events automatically constitute a compatible public API.

The verified material supports this architecture and highlights a critical testing lesson: a successful demonstration of one request proves only a narrow form of transport compatibility. It does not show that the endpoint honors the full request surface, preserves expected response fields, streams valid semantics, or behaves correctly through a public gateway. The steps below therefore focus on defining and validating that contract rather than asserting a particular unverified codebase, model version, database, or approval policy.

Prerequisites and scope

• Familiarity with HTTP request and response formats.
• Basic understanding of FastAPI routes and LangGraph workflows.
• A clearly documented client-facing request and response contract.
• A test plan for both non-streaming responses and SSE, if streaming is part of the interface.

The available sources do not specify a current package matrix, model availability, dependency pins, a vector database, or a particular RAG design. This tutorial intentionally does not invent them. Select versions and infrastructure for your deployment and verify them against the applicable official documentation before implementation.

Step 1: Define the public contract before building the graph adapter

Write down what clients are allowed to send and what they are entitled to receive. For an OpenAI-compatible endpoint, begin with the request shape your intended clients use, including the fields your service will support. Then define the response shape, including which response fields the adapter will return and how it represents the final graph output.

Do not equate a familiar-looking JSON body with complete compatibility. The verified compatibility analysis makes this distinction explicit: accepting an OpenAI-shaped Chat Completions request, invoking a graph, and returning a choices array demonstrates a happy path for one request. It does not, by itself, prove that the endpoint honors the broader request surface or preserves response fields clients rely on.

Create a contract checklist that answers these questions before implementation:

• Which request fields will the endpoint accept?
• Which fields will be validated, used, ignored, or rejected?
• How will the adapter translate an accepted request into graph input?
• Which response fields will be returned, and how will the final graph message map to them?
• What should a client observe when the request is invalid or the graph cannot provide a final result?
• Will the endpoint support streaming, non-streaming responses, or both?

These are design and test questions, not claims that a particular implementation automatically answers them. Record the intended behavior and test it. Avoid silently claiming support for fields or behaviors that the adapter does not implement.

Step 2: Keep the API adapter separate from LangGraph state

Use a boundary layer to translate between the public protocol and the application workflow. Conceptually, the service has five parts: an OpenAI client sends a request; a FastAPI route receives it; a validator and compatibility adapter check and translate it; the adapter invokes LangGraph; and a response assembler serializes the result. The response assembler can then select a non-streaming serializer or an SSE serializer, depending on the request and the service’s documented behavior.

Think of this as a responsibility map rather than an implementation recipe:

1. Request validation: Check that the incoming public request matches the contract the endpoint actually supports.
2. Request translation: Convert accepted public fields into the application input expected by the graph.
3. Workflow execution: Let LangGraph represent application orchestration, rather than forcing internal graph state to...
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