API discovery: LangChain's new public interface adds reasoning_effort at model construction and invocation across supported OpenAI, Anthropic, Fireworks, and xAI integrations. Its ChatOpenAI reference documents the parameter for reasoning-capable OpenAI chat models. The useful distinction is between a common request from the application and a common provider API: LangChain does the translation, while each model profile says what it can actually accept.
Try it
Use it in a release-triage assistant. Let the assistant cluster routine issue reports at a lower supported effort, then ask for a higher supported effort only when it has to reconcile a suspected breaking change across changelog, migration guide, and tests. That makes the effort choice visible in the task rather than scattering provider-specific switches through the codebase.
Copy-paste agent instruction
In this LangChain Python project, find the configured chat-model integration and inspect its model profile for supported reasoning-effort levels before changing code. Add a `reasoning_effort` value only when the selected model advertises support. Use a lower supported level for routine release-note grouping and a higher supported level only for the final breaking-change reconciliation. Keep provider-specific request fields out of application code, run the relevant integration tests, and report the exact model, accepted level, and any unsupported-provider caveat.Test caveat
You need valid provider credentials, the relevant LangChain integration, and a model that advertises a compatible effort setting. The patch maps the common argument differently for different providers; it is not evidence that every provider, model, value, price point, or latency target behaves alike. Its integration tests are useful receipts, but test the exact model in a safe environment before putting an effort policy into production.