810def4fc5
New `langchain_tests.utils.stream_lifecycle.assert_valid_event_stream` helper enforces the protocol contract on any event stream: - single message-start / message-finish envelope - blocks do not interleave (each block finishes before the next starts) - sequential uint wire indices from 0 - accumulated deltas match the finish payload for deltaable types Applied at three levels: - core/test_compat_bridge: provider-style emission patterns exercised directly through chunks_to_events / message_to_events (openai chat completions int indices, openai responses/v1 string identifiers, anthropic-style per-chunk int indices, inline image, invalid tool call, empty stream) - openai partner: validator applied to stream_v2 against the existing responses-api mock and to a new chat-completions stream_v2 test - anthropic partner: new mock stream of RawMessageStartEvent + RawContentBlock* events threaded through _stream via `_create` patch; covers thinking + text + tool_use lifecycle with tool-use stop_reason Enabling thinking on the anthropic test flips coerce_content_to_string off so every block carries a proper integer index — the structured path the bridge actually exercises. Default-mode (no tools / thinking / docs) coerces text to a plain string and strips per-chunk indices; the bridge handles that branch by collapsing to positional-0 and it is a known separate code path, intentionally not covered here.
🦜🍎️ 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.