63ca3f2831
Extend the v2 stream and compat bridge to handle every protocol ContentBlock variant end-to-end — server tool calls, invalid tool calls, images, audio, video, file, and non-standard blocks — not just text, reasoning, and regular tool calls. Previously these were silently dropped at the bridge's extractor, had no handler in ChatModelStream, and could not appear in .output.content. The stream now keeps an index-ordered `_blocks` snapshot as the single source of truth for .output.content, alongside the existing typed accumulators that drive the public projections. `_assemble_message` builds content from that snapshot, emitting protocol-shape `tool_call` blocks instead of the legacy `tool_use` shape, and collapses to a bare string only when the message contains exactly one text block. Bridge extractors (_extract_blocks_from_chunk, _extract_final_blocks) now pass through any protocol-shape block in msg.content, _accumulate_block and _delta_block handle server_tool_call_chunk and self-contained types, and _finalize_block promotes server_tool_call_chunk to server_tool_call (falling back to invalid_tool_call on JSON failure, symmetric with regular tool calls). The standard `invalid_tool_calls` field on AIMessage is also surfaced by the final-block extractor. Forward-looking: today's partners keep provider-native shapes in msg.content and expose protocol blocks lazily via the `.content_blocks` property, so these paths are latent until partners either populate msg.content with protocol shape or override _stream_chat_model_events. The bridge is ready.
🦜🍎️ 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.