6b9e22dbbc
Provider-native structured output fallback detection now uses bounded model-name patterns instead of broad substring checks, reducing false positives for unrelated model IDs. The model examples and test fixtures across OpenAI/OpenRouter-facing code were refreshed around current OpenAI model families while preserving shipped defaults. ## Changes - Tightened `FALLBACK_MODELS_WITH_STRUCTURED_OUTPUT` from loose string fragments to regex patterns, with `_supports_provider_strategy` matching full model-name segments instead of arbitrary substrings. - Expanded structured-output fallback coverage for newer OpenAI, Anthropic, and xAI/Grok model families, including `gpt-5.x`, newer Claude 4/5-style names, and `grok-build`. - Reused `_attempt_infer_model_provider` in provider tool search routing so `_provider_from_model_name` follows the same provider inference behavior as `init_chat_model`. - Suppressed irrelevant provider-inference deprecation warnings during provider tool search registry lookup. - Refreshed OpenAI, Azure OpenAI, OpenRouter, core metadata, and example model references from older fixtures like `gpt-4`, `gpt-4o`, `o1`, and `o4-mini` to current test/profile models such as `gpt-5.5`, `gpt-5-nano`, and `gpt-4.1-mini`. - Removed outdated OpenAI test assumptions around legacy `o1` behavior and narrowed legacy structured-output checks to explicitly legacy model names.
🦜🍎️ 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
pip install 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.