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⚡ Building applications with LLMs through composability ⚡
dcb5aba999
Scheduled testing started failing today because the Responses API stopped raising `BadRequestError` for a schema that was previously invalid when `strict=True`. Although docs still say that [some type-specific keywords are not yet supported](https://platform.openai.com/docs/guides/structured-outputs#some-type-specific-keywords-are-not-yet-supported) (including `minimum` and `maximum` for numbers), the below appears to run and correctly respect the constraints: ```python import json import openai maximums = list(range(1, 11)) arg_values = [] for maximum in maximums: tool = { "type": "function", "name": "magic_function", "description": "Applies a magic function to an input.", "parameters": { "properties": { "input": {"maximum": maximum, "minimum": 0, "type": "integer"} }, "required": ["input"], "type": "object", "additionalProperties": False }, "strict": True } client = openai.OpenAI() response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}], tools=[tool], ) function_call = next(item for item in response.output if item.type == "function_call") args = json.loads(function_call.arguments) arg_values.append(args["input"]) print(maximums) print(arg_values) # [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # [1, 2, 3, 3, 3, 3, 3, 3, 3, 3] ``` Until yesterday this raised BadRequestError. The same is not true of Chat Completions, which appears to still raise BadRequestError ```python tool = { "type": "function", "function": { "name": "magic_function", "description": "Applies a magic function to an input.", "parameters": { "properties": { "input": {"maximum": 5, "minimum": 0, "type": "integer"} }, "required": ["input"], "type": "object", "additionalProperties": False }, "strict": True } } response = client.chat.completions.create( model="gpt-4.1", messages=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}], tools=[tool], ) response # raises BadRequestError ``` Here we update tests accordingly. |
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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.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.