Endpoint
Use a compatible API endpoint
The common pattern is to keep your application unchanged and point it to the relay’s OpenAI-compatible base URL.
For this guide, the config example uses https://59api.com/v1. That means your SDK can continue calling familiar
chat, responses, or embeddings routes while traffic is proxied through the relay.
What to check before adopting
Look for clear request logging, model naming consistency, predictable latency, and transparent按量付费 billing. Also confirm how the relay handles retries, streaming, and error pass-through so your app can fail gracefully.
A solid relay should behave like a thin compatibility layer, not a rewrite of your stack. In practice, that means your app should only need environment variable changes and a quick verification pass.
Headers
Most OpenAI-style clients only need a small set of headers. The exact names depend on your SDK, but the core idea is simple: authenticate once, keep the request structure standard, and let the relay forward traffic to the upstream provider.
Recommended headers
Authorization: Bearer YOUR_KEYContent-Type: application/json- Optional request identifiers for tracing
Smoke-test criteria
- HTTP 200 on a small chat request
- Streaming response arrives without buffering issues
- Model name is accepted by the relay
- Error messages remain readable for debugging
Example
Below is a simple environment configuration example. It is intentionally short so you can copy it into local development, CI, or a container entrypoint.
OPENAI_BASE_URL=#/v1
OPENAI_API_KEY=your_api_key_here
OPENAI_MODEL=gpt-4.1-miniSmoke-test steps: first, load the environment variables and restart your app. Second, send a tiny prompt such as “Say hello in one sentence.” Third, confirm the SDK reaches the relay endpoint, receives a response, and does not require code changes beyond the base URL. If your client supports streaming, test that path as well because it often exposes proxy or timeout issues earlier than a normal request.
When evaluating an AI API relay, the best sign is operational simplicity: you can keep your application logic intact, switch the base URL, and measure whether latency, routing, and usage costs remain understandable. If you need a starting point, visit the relay site and compare the integration notes with your current SDK. For teams managing multiple services, a relay can also centralize model access, which makes auditing and debugging easier.
FAQ
Is this only for OpenAI clients?
No. Any tool that supports an OpenAI-compatible base URL can usually work with the same relay pattern, as long as it respects standard request and response formats.
What is the fastest way to verify the setup?
Use one minimal request with a known model, check the HTTP status, then repeat with streaming enabled. If both pass, your relay configuration is likely correct.
Do I need to change my code?
In many cases, no. Updating OPENAI_BASE_URL and your API key is enough. Some SDKs may also need a model name or timeout adjustment.