Unify every LLM API behind one endpoint

Most teams end up talking to several model providers — a cheap model for bulk jobs, a strong one for hard prompts, a fast one for latency-sensitive paths. Each provider brings its own SDK, base URL, auth header, request body, streaming format, and error codes. That surface area is a tax you pay on every new model and every upgrade.

• A new SDK and auth flow per provider you add.
• No single place to see spend, latency, or error rates across vendors.
• Swapping a model for cost or quality means touching code and redeploying.
• An outage at one provider becomes an outage for your product.

Unifying means one OpenAI-compatible contract in front of every provider. Model ids become `provider/model` strings, so switching a model is a config value — not an integration. You also get one key, one bill, and one audit log across all of them, plus automatic failover when an upstream is down.

from openai import OpenAI

client = OpenAI(base_url="https://anyrouter.dev/v1", api_key="sk-ar-v1-...")

for model in ["openai/gpt-5.5", "anthropic/claude-opus-4-8", "google/gemini-2.5-pro"]:
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Hello"}],
    )

A unifying layer is only safe if leaving it is easy. Because the contract is the standard OpenAI API, the same code points straight back at any provider — there's no proprietary SDK to rip out later.