43880362d8
As a LangChain user streaming a tool-calling model, I expect each streamed chunk to expose structured `tool_call_chunk` content blocks so I can render or process tool calls live, instead of waiting for the final aggregated message. This adds `tool_call_streaming` to `ModelProfile` and uses it in the standard chat-model tool-calling tests. When a model profile opts in, `test_tool_calling` and `test_tool_calling_async` now validate that at least one streamed chunk includes a `tool_call_chunk` block via `content_blocks`, while preserving the existing final-message validation. This keeps the contract profile-gated so providers can opt in once their streaming chunk shape is verified. This PR opts in the providers verified by smoke testing with straightforward profile coverage: OpenAI, Anthropic, Fireworks, HuggingFace, OpenRouter, DeepSeek, and xAI. The generated profile artifacts are refreshed so runtime profiles expose the new capability flag. Perplexity Responses also passed the smoke test, but its current profile data is for the `sonar` family while the Responses smoke path used a routed model string. That profile strategy is left as follow-up. MistralAI currently streams `.tool_call_chunks`, but its content-block translator exposes a complete `tool_call` block instead of `tool_call_chunk`, so it also stays out of this flag until that integration is fixed.
🦜🍎️ 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.