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PR #35788 added 7 new fields to the `langchain-profiles` CLI output (`name`, `status`, `release_date`, `last_updated`, `open_weights`, `attachment`, `temperature`) but didn't update `ModelProfile` in `langchain-core`. Partner packages like `langchain-aws` that set `extra="forbid"` on their Pydantic models hit `extra_forbidden` validation errors when Pydantic encountered undeclared TypedDict keys at construction time. This adds the missing fields, makes `ModelProfile` forward-compatible, provides a base-class hook so partners can stop duplicating model-profile validator boilerplate, migrates all in-repo partners to the new hook, and adds runtime + CI-time warnings for schema drift. ## Changes ### `langchain-core` - Add `__pydantic_config__ = ConfigDict(extra="allow")` to `ModelProfile` so unknown profile keys pass Pydantic validation even on models with `extra="forbid"` — forward-compatibility for when the CLI schema evolves ahead of core - Declare the 7 missing fields on `ModelProfile`: `name`, `status`, `release_date`, `last_updated`, `open_weights` (metadata) and `attachment`, `temperature` (capabilities) - Add `_warn_unknown_profile_keys()` in `model_profile.py` — emits a `UserWarning` when a profile dict contains keys not in `ModelProfile`, suggesting a core upgrade. Wrapped in a bare `except` so introspection failures never crash model construction - Add `BaseChatModel._resolve_model_profile()` hook that returns `None` by default. Partners can override this single method instead of redefining the full `_set_model_profile` validator — the base validator calls it automatically - Add `BaseChatModel._check_profile_keys` as a separate `model_validator` that calls `_warn_unknown_profile_keys`. Uses a distinct method name so partner overrides of `_set_model_profile` don't inadvertently suppress the check ### `langchain-profiles` CLI - Add `_warn_undeclared_profile_keys()` to the CLI (`cli.py`), called after merging augmentations in `refresh()` — warns at profile-generation time (not just runtime) when emitted keys aren't declared in `ModelProfile`. Gracefully skips if `langchain-core` isn't installed - Add guard test `test_model_data_to_profile_keys_subset_of_model_profile` in model-profiles — feeds a fully-populated model dict to `_model_data_to_profile()` and asserts every emitted key exists in `ModelProfile.__annotations__`. CI fails before any release if someone adds a CLI field without updating the TypedDict ### Partner packages - Migrate all 10 in-repo partners to the `_resolve_model_profile()` hook, replacing duplicated `@model_validator` / `_set_model_profile` overrides: anthropic, deepseek, fireworks, groq, huggingface, mistralai, openai (base + azure), openrouter, perplexity, xai - Anthropic retains custom logic (context-1m beta → `max_input_tokens` override); all others reduce to a one-liner - Add `pr_lint.yml` scope for the new `model-profiles` package
The agent engineering platform.
LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
Note
Looking for the JS/TS library? Check out LangChain.js.
Quickstart
pip install langchain
# or
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.4")
result = model.invoke("Hello, world!")
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangChain ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
- Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations — Chat & embedding models, tools & toolkits, and more
- LangSmith — Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
- Real-time data augmentation — Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more
- Model interoperability — Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly — LangChain's abstractions keep you moving without losing momentum
- Rapid prototyping — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
- Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
- Vibrant community and ecosystem — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community
- Flexible abstraction layers — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python – API reference docs for LangChain packages
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
- LangChain Academy – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.