OpenAI-compatible APIs and clients

Most LLM client libraries and agent frameworks speak one request format for chat. An API is called OpenAI-compatible when it accepts that format and answers in the same shape. Your code does not change. Three settings do: the base URL, the API key and the model name.

Compatibility is what makes a comparison cheap to act on. If two APIs take the same request, you can send the same prompts to both in an afternoon and read the answers side by side. If one needs its own client, every test is a small integration project.

It also keeps you free to leave. Code written against a common format moves to another host when prices change.

The basic chat request is nearly always the same. The edges are where APIs differ: tool calls, structured output, streaming details, image input and the names of optional settings. An API can be compatible for plain chat and still break an agent that relies on tool calls.

So test the features you use, not only a hello. Run one real request of each kind through the API before you commit.

Put the base URL, the key and the model name in configuration, not in code. Point them at the new API. Run your own test prompts. Compare the answers, the token counts returned and the errors.

Keep the old settings until the new ones have run a full day of real traffic.

Runcrate puts 124 models behind one OpenAI-compatible API. Changing models is a change of model name. Runcrate also has a CLI, an SDK and an MCP server, so an agent can use the platform as well as the models.

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