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`langchain-anthropic`, `langchain-openai`, `langchain-fireworks`, and `langchain-xai` now support a standard `reasoning_effort` parameter. It can be set at model construction or per invocation, with each provider translating it to the appropriate API. `ModelProfile` now exposes supported reasoning effort levels and the default level for supported models. --- Adds a standard `reasoning_effort` parameter across chat model integrations. Like `temperature`, it can be set on the model or per call, while each provider translates it into its own API format. Motivating example: [`deepagents-code` (dcode)](https://docs.langchain.com/oss/python/deepagents/code/overview) implemented provider-agnostic reasoning effort itself. This PR upstreams that support into LangChain's provider integrations, making `reasoning_effort` a standard parameter and exposing supported levels via `ModelProfile`. ```python from langchain_anthropic import ChatAnthropic model = ChatAnthropic(model="claude-sonnet-4-6") model.invoke( "Why do parrots have colorful feathers?", reasoning_effort="high", ) ``` ### Model specific labels and default | Provider | Model / version | Levels supported | Default | |----------|-----------------|------------------|---------| | **OpenAI** | gpt-5.5, gpt-5.6* | `none`, `low`, `medium`, `high`, `xhigh` (+ `max` on gpt-5.6+) | `medium` | | | other gpt-5* | `none`, `low`, `medium`, `high`, `xhigh` | — | | **Anthropic** | Opus 4.0 / 4.1 | *(none – predates effort entirely)* | — | | | Opus 4.5 | `low`, `medium`, `high` | `high` | | | Opus 4.6 | `low`, `medium`, `high`, `max` | `high` | | | Opus 4.7+ | `low`, `medium`, `high`, `xhigh`, `max` | `high` | | | Sonnet 4.0 / 4.1 / 4.5 | *(none – predates or rejects effort)* | — | | | Sonnet 4.6 | `low`, `medium`, `high`, `max` | `high` | | | Sonnet 5+ | `low`, `medium`, `high`, `xhigh`, `max` | `high` | | **Fireworks** | DeepSeek V4 Pro | `none`, `low`, `medium`, `high`, `xhigh`, `max` | `high` | | | Kimi K2 | `low`, `medium`, `high` | — | | | GLM 5 | `none`, `high`, `max` | `max` | | **xAI** | Grok 4.5-class models | `low`, `medium`, `high` | `high` | ### Provider specific details | Provider | Model class | Translation | |----------|-------------|-------------| | **OpenAI** | `ChatOpenAI` | Translates `reasoning_effort` to `reasoning.effort` and adds `summary: "auto"`. | | **Anthropic** | `ChatAnthropic` | Translates `reasoning_effort` to `output_config.effort` and defaults `thinking` to `adaptive`. | | **Fireworks** | `ChatFireworks` | Sends `reasoning_effort` as a flat field unchanged. | | **xAI** | `ChatXAI` | Nests `reasoning_effort` under `extra_body.reasoning_effort`. | ### Anthropic: `effort` alias `ChatAnthropic` already exposed an `effort` parameter. This PR makes it a true Pydantic alias for `reasoning_effort` while preserving existing behavior. - Both `effort` and `reasoning_effort` work at construction; if both are provided, `effort` wins. - Call-time (`.invoke()`, `.bind()`) support is handled manually to preserve the same precedence, since Pydantic aliases only apply at construction. - `effort` remains supported (no deprecation). ### Other providers - **DeepSeek**: inherits support from `BaseChatOpenAI`. - **Groq** and **Perplexity**: already have native `reasoning_effort` support. - **OpenRouter** and **Mistral AI**: out of scope (no native support today). - **Google (Gemini)**: implemented separately in [`langchain-google`](https://github.com/langchain-ai/langchain-google/pull/1895)
🦜🍎️ LangChain Core
Looking for the JS/TS version? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.
Quick Install
uv add langchain-core
🤔 What is this?
LangChain Core contains the base abstractions that power the LangChain ecosystem.
These abstractions are designed to be as modular and simple as possible.
The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.
⛰️ Why build on top of LangChain Core?
The LangChain ecosystem is built on top of langchain-core. Some of the benefits:
- Modularity: We've designed Core around abstractions that are independent of each other, and not tied to any specific model provider.
- Stability: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
- Battle-tested: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
📖 Documentation
For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangChain, see the LangChain Docs. You can also chat with the docs using Chat LangChain.
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.
Resources
- LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
- Code of Conduct — community guidelines and standards