configurable_model

Project the params that participate in prompt-cache identity.

Declared context_schema for the agent graph.

Registered via context_schema= when the graph is built, so LangGraph coerces each run's context= payload into this dataclass — in-process, runtime.context is a CLIContextSchema instance.

It exists alongside CLIContext (below) because the payload is shaped differently on each side of the API boundary: in-process it is coerced to this dataclass, but over the LangGraph API server (RemoteGraph) it is serialized to JSON and arrives as a plain dict. Consumers (configurable_model._get_context, _should_interrupt_tool_call) therefore accept both shapes. CLIContext is the client-facing builder for constructing that payload.

Fields mirror CLIContext; see its per-field docstrings for semantics.

A cache-using request's start time and effective cache identity.

Result of creating a chat model, bundling the model with its metadata.

This separates model creation from runtime-state mutation so callers can decide when to commit the metadata to process-wide state.

Swap the model or per-call settings from runtime.context.

Reads two optional keys from the runtime context dict:

• 'model' — a provider:model spec (e.g. "openai:gpt-5"). When present and different from the current model, the request is re-routed to the new model.
• 'model_params' — a dict of extra model settings (e.g. {"temperature": 0}) that are shallow-merged into the request's model_settings.

This middleware is typically the outermost layer so it intercepts every model call before provider-specific middleware (like AnthropicPromptCachingMiddleware) runs.