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# Description This PR fixes a bug in _recursive_set_additional_properties_false used in function_calling.convert_to_openai_function. Previously, schemas with "additionalProperties=True" were not correctly overridden when strict validation was expected, which could lead to invalid OpenAI function schemas. The updated implementation ensures that: - Any schema with "additionalProperties" already set will now be forced to False under strict mode. - Recursive traversal of properties, items, and anyOf is preserved. - Function signature remains unchanged for backward compatibility. # Issue When using tool calling in OpenAI structured output strict mode (strict=True), 400: "Invalid schema for response_format XXXXX 'additionalProperties' is required to be supplied and to be false" error raises for the parameter that contains dict type. OpenAI requires additionalProperties to be set to False. Some PRs try to resolved the issue. - PR #25169 introduced _recursive_set_additional_properties_false to recursively set additionalProperties=False. - PR #26287 fixed handling of empty parameter tools for OpenAI function generation. - PR #30971 added support for Union type arguments in strict mode of OpenAI function calling / structured output. Despite these improvements, since Pydantic 2.11, it will always add `additionalProperties: True` for arbitrary dictionary schemas dict or Any (https://pydantic.dev/articles/pydantic-v2-11-release#changes). Schemas that already had additionalProperties=True in such cases were not being overridden, which this PR addresses to ensure strict mode behaves correctly in all cases. # Dependencies No Changes --------- Co-authored-by: Zhong, Yu <yzhong@freewheel.com>
Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building 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.
pip install -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- 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.
LangChain’s 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.
To improve your LLM application development, pair LangChain with:
- LangSmith - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- LangGraph Platform - Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.
- Chat LangChain: Ask questions & chat with our documentation.