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Add `BaseChatModel.stream_v2()` / `astream_v2()` returning a `ChatModelStream` with typed projections (`.text`, `.reasoning`, `.tool_calls`, `.usage`, `.output`) plus raw protocol event iteration. Providers that only implement `_stream()` get a compat bridge that converts `AIMessageChunk`s to the content-block protocol lifecycle, preserving usage and response metadata for v1 parity. - New module `chat_model_stream.py` with `ChatModelStream`, `AsyncChatModelStream`, and push/pull projection hierarchy (`SyncProjection`, `SyncTextProjection`, `AsyncProjection`). - New module `_compat_bridge.py` that converts chunk streams to protocol events, with `response_metadata` preserved via `MessageStartData.metadata` and `MessageFinishData.metadata`. - `stream_v2` wires `on_chat_model_start` / `on_llm_end` / `on_llm_error` callbacks into the pump; `astream_v2` spawns a producer task and awaits it alongside the output so `on_llm_end` fires before `await stream` returns. - tool_use finish-reason inference runs after finalization so malformed tool-call JSON (finalized as `invalid_tool_call`) does not flip `finish_reason` to `"tool_use"`. - Add `langchain-protocol>=0.0.6` dependency (local path override retained for dev). Tests cover projection semantics, tool-call streaming (single + parallel + malformed args), async/sync event replay, callback firing, and v1 parity (text, tool calls, usage, response metadata, reasoning+text ordering, error propagation).
🦜🍎️ LangChain Core
Looking for the JS/TS version? Check out LangChain.js.
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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.