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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.
The agent engineering platform.
LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
Tip
Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
Quickstart
pip install langchain
# or
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.4")
result = model.invoke("Hello, world!")
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
For an equivalent JS/TS library, check out LangChain.js.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangChain ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
- Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations — Chat & embedding models, tools & toolkits, and more
- LangSmith — Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
- Real-time data augmentation — Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more
- Model interoperability — Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly — LangChain's abstractions keep you moving without losing momentum
- Rapid prototyping — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
- Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
- Vibrant community and ecosystem — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community
- Flexible abstraction layers — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python – API reference docs for LangChain packages
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
- LangChain Academy – Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.