f42d80ca1c
The v3 streaming path drops `additional_kwargs` from per-chunk `AIMessageChunk`s during assembly: `chunks_to_events` emits no event field for them, and `ChatModelStream._assemble_message` constructs the final `AIMessage` without an `additional_kwargs` argument. Non-streaming `ainvoke` returns the provider message unchanged, so streaming and non-streaming diverge for any provider that uses `additional_kwargs` to carry data outside the typed protocol blocks. ## How this surfaces The concrete failure mode is Gemini's `__gemini_function_call_thought_signatures__` — a per-tool-call signature blob the Google GenAI integration places in `additional_kwargs`, keyed by `tool_call_id`. Gemini requires that signature on follow-up turns to replay the prior thought trace; without it, multi-turn streaming flows lose thought continuity (and may regenerate thinking, charging additional reasoning tokens, or in some cases refuse). Other providers that use `additional_kwargs` (e.g. older `function_call` accumulators, custom routing metadata) hit the same gap; the fix is intentionally provider-agnostic. ## Fix Provider-agnostic, two seams: - `_compat_bridge` accumulates `msg.additional_kwargs` across chunks with `merge_dicts` (matching `AIMessageChunk`'s own merge semantics for fields that accumulate, like `function_call`) and emits the merged dict on the `message-finish` event as an off-spec extension. The bridge already uses one such extension (`metadata` on `MessageFinishData`); this PR follows the same pattern for `additional_kwargs`. - `ChatModelStream._finish` reads the new field; `_assemble_message` threads it onto the final `AIMessage` only when non-empty, preserving today's behavior of leaving `additional_kwargs` empty when no provider data needs to ride on it.
🦜🍎️ 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.