API reference stylePractical guide

AI API Relay: a clean reference for OpenAI-compatible routing and setup

If you need a simple way to route AI requests through an AI API relay, this page shows the parts that matter: endpoint shape, required headers, a smoke-test workflow, and a working config example. It is written for developers who want predictable integration details rather than marketing fluff.

Open relay homepage View OpenAI-compatible relay

Endpoint

Treat the relay as an OpenAI-compatible entry point. In practice, that means your app should send requests to a base URL that looks like the standard OpenAI API, then keep your model, messages, and authentication flow familiar. For teams that are evaluating an API中转站, the main question is whether the relay preserves the same request and response conventions your tooling already expects.

A good relay should work for common smoke tests first: list a model, send a short chat completion, and confirm the response format. For developers working on 国内直连Claude style connectivity, the value is less about the label and more about whether the route is simple enough to automate, monitor, and swap without code rewrites.

Base URL:
https://59api.com/v1

Typical request path:
POST /chat/completions

Smoke-test goal:
Send a short prompt and verify a normal JSON response.

Headers

Authorization Use a bearer token in the standard OpenAI format.

Content-Type Keep requests as application/json so client libraries behave normally.

Compatibility check Start with the same headers you would use against an OpenAI-compatible relay.

The best integration test is boring in the right way: use the same header structure you already trust, then compare the relay’s output with your baseline client. If logging is enabled in your app, capture request IDs, latency, and the first failure code. Those three signals are usually enough to tell whether an issue is in your prompt, network path, or auth config.

Example

# Environment variables
OPENAI_API_KEY=your_token_here
OPENAI_BASE_URL=#/v1

# Python example
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
    base_url=os.getenv("OPENAI_BASE_URL")
)

resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role":"user","content":"Say hello in one sentence."}]
)
print(resp.choices[0].message.content)

Smoke-test steps

1. Set the base URL and API key in your environment.

2. Call a simple chat endpoint with one short message.

3. Confirm the response is JSON, the model field is present, and the content is readable.

4. If the test fails, check for auth mismatch, wrong path, or proxy/network blocking before changing prompts.

What to look for

Successful relays usually behave like a normal API surface: stable request shapes, familiar errors, and minimal client changes. That is what makes an AI API relay useful in production and in local integration testing.

FAQ

Is this only for one SDK?

No. Any client that supports an OpenAI-compatible base URL can usually be adapted with a small config change.

Why use a relay instead of changing application code?

A relay reduces integration drift. You keep the same call structure and swap the endpoint, which is easier to test and maintain.

What should I verify first in production?

Start with auth, latency, and error consistency. If those are stable, the rest of your workflow is much easier to trust.

Go to 59API