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yujonglee/openrouter-toolkit

 mainBranchesTagsGo to fileCodeOpen more actions menuFolders and filesNameNameLast commit messageLast commit dateLatest commit History9 Commits9 Commits.github/workflows.github/workflows  cratescrates  srcsrc  teststests  .gitignore.gitignore  .oxfmtrc.json.oxfmtrc.json  Cargo.lockCargo.lock  Cargo.tomlCargo.toml  README.mdREADME.md  package.jsonpackage.json  pnpm-lock.yamlpnpm-lock.yaml  rust-toolchain.tomlrust-toolchain.toml  View all filesRepository files navigationREADMEopenrouter-toolkit
Compile-time checked OpenRouter model ids.
model_supports! validates a model id and its required capabilities against a vendored OpenRouter index, then expands to the model id string.
use openrouter_toolkit::model_supports;

const MODEL: &str = model_supports!(
"openai/gpt-5.4",
param::tools,
input::image,
output::text,
);
Dynamic variants work too:
const MODEL: &str = model_supports!("moonshotai/kimi-k2-0905:exacto", param::tools);
Capabilities

param::* — request parameters (e.g. param::tools)
input::* — input modalities (e.g. input::image)
output::* — output modalities (e.g. output::text)

Errors at compile time
Unknown capability:
const MODEL: &str = model_supports!("qwen/qwen3.7-max", param::toolz);
error: unknown OpenRouter capability `param::toolz`; did you mean `param::tools`?

Capability not supported by the model:
const MODEL: &str = model_supports!("qwen/qwen3.7-max", input::image);
error: OpenRouter model `qwen/qwen3.7-max` does not support required capability(s): input::image

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The openrouter-toolkit is a project implemented primarily in Rust, designed to validate and manage the supported capabilities of various AI models indexed via the OpenRouter platform. The core functionality revolves around a mechanism, specifically the model_supports function, which validates a given model identifier against a vendored OpenRouter index to determine if it supports a specific set of required and desired model capabilities.

The system models capabilities using structured parameters. Input modalities, such as input::image, and output modalities, such as output::text, are defined to specify the types of data the model can process or generate. Request parameters, like param::tools, are used to specify functional capabilities. The toolkit determines the compatibility of a model by cross-referencing its advertised capabilities against the requirements specified by the user.

The validation process is designed to occur at compile time, ensuring that model configurations are sensible before runtime execution. The system manages this validation by checking for the presence of requested capabilities. For instance, the toolkit allows users to define a configuration for a model, such as requiring tool usage, input image processing, or text output.

The system exhibits compile-time error handling for invalid configurations. If a requested capability, such as param::toolz, is unknown within the OpenRouter index, the compiler will immediately flag an error, suggesting alternative valid options, preventing the creation of logically inconsistent model definitions. Furthermore, the system enforces explicit support requirements; if a model, such as qwen/qwen3.7-max, is determined not to support a requested modality, such as input::image, the compilation fails with an error indicating that the model lacks the required capability.

The toolkit supports both static and dynamic model referencing. It can validate known model IDs directly, such as openai/gpt-5.4, or dynamically constructed identifiers incorporating specific model tags, such as moonshotai/kimi-k2-0905:exacto. This dynamic approach allows the toolkit to adapt its validation logic across a diverse set of AI providers indexed by OpenRouter.

In essence, the openrouter-toolkit provides a robust framework for interacting with and verifying the operational constraints of large language models by formalizing their supported input, output, and functional capabilities against a centralized index. This mechanism enforces a contract between the desired application requirements and the actual model specifications, thereby enhancing the reliability of AI-powered workflows.