One conversation shape. The model is the routing address.

Write a conversation once:

{
  "model": "openai/gpt-5.6-sol",
  "messages": [
    { "role": "user", "content": "Explain ownership in one sentence." }
  ]
}

To send the same conversation to another provider, change one line:

- "model": "openai/gpt-5.6-sol"
+ "model": "anthropic/claude-opus-4-8"

llmshim selects the provider, translates the request into that provider's native API, sends it, and translates the result back. Change the model, not the conversation. That is the central idea.

At its core, llmshim is a Rust library. The CLI and HTTP proxy wrap the same library. The Python, TypeScript, Go, and Ruby clients are thin clients for the proxy; Python and TypeScript can also start a bundled proxy automatically.

flowchart LR
    Rust[Rust application] --> Core[llmshim Rust engine]
    Terminal[Terminal user] --> CLI[llmshim chat]
    CLI --> Core
    HTTP[HTTP callers] --> Proxy[llmshim proxy]
    Clients[Language clients] --> Proxy
    Proxy --> Core
    Core --> Providers[OpenAI / Anthropic / Gemini / xAI]

A translation boundary

Think of llmshim like a compiler with provider-specific backends. The input is an OpenAI Chat Completions-style conversation. The model string selects a backend. That backend translates the request to the provider's native API and normalizes the provider's response.

flowchart LR
    Request[Model + messages + controls] --> Router[Resolve model address]
    Router --> Outbound[Translate request]
    Outbound --> API[Provider-native API]
    API --> Inbound[Translate response]
    Inbound --> Result[Normalized result]

Three rules define the boundary:

1. Common features are translated where the target supports them. Messages, tools, images, streaming, and reasoning controls all pass through a provider-specific adapter.
2. Different APIs do not become identical. A provider may support fewer reasoning levels, require another image representation, or omit a feature. llmshim maps, clamps, or omits fields according to the target.
3. Native controls remain available. Use x-openai, x-anthropic, or x-gemini when a portable control is not enough. There is no x-xai namespace.

llmshim is not an agent framework. It does not execute tools or own conversation memory. Your application remains responsible for both.

Next, choose the surface that fits your application, or see how translation flows in more detail.