2c449ca1f5
Collapse _compat_bridge to a single path that reads msg.content_blocks and emits protocol events. The translator / best-effort / tool_call_chunks extraction all live in content_blocks already — the legacy branch, _PROTOCOL_PASS_THROUGH_TYPES, _SELF_CONTAINED_BLOCK_TYPES skeleton handling, and manual reasoning-variant sniffing were duplicating work. Side fixes picked up along the way: - No-provider chunks with both text content and tool_call_chunks silently dropped the tool call because the legacy extractor put both at index 0. content_blocks places them on distinct indices. - "server_tool_call_result" (typo) replaced with "server_tool_result" in ChatModelStream's finish dispatch and the test that exercises it — matches the protocol type that every translator actually emits. Also collapses duplicated tool_call_chunk / server_tool_call_chunk handling in chat_model_stream into shared merge/sweep helpers so the two code paths can't drift apart again (which is how the typo survived). _compat_bridge.py: 855 -> 581 lines. No public API changes.
🦜🍎️ 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.