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Supersedes #34727 Closes #30703 Related: * langchain-ai/langchain-google#1460 * langchain-ai/langchain-google#1501 Fixing this at the `langchain-core` callback layer instead of normalizing inside individual provider integrations, so structured streaming content is preserved consistently. --- Models are increasingly streaming structured content blocks instead of plain text tokens. For example, Gemini 3 can stream text as content-block lists, and Anthropic/tool-use flows can also produce non-text message content. Today those values already reach `on_llm_new_token`, but the callback API still advertises `token: str`, which makes custom callbacks, tracers, and streaming helpers assume every streamed value is text. User story: as a LangChain user building a streaming callback for chat models with tool calls, reasoning/thinking blocks, or provider-specific structured content, I need `on_llm_new_token` to accept the same content shape that chat model chunks can actually emit, so my callback can observe the stream without providers flattening or dropping non-text data. Fixing this in `langchain-core` makes the existing runtime behavior explicit at the shared callback boundary. Normalizing content blocks inside each provider would duplicate logic, produce inconsistent behavior across integrations, and in some cases lose required provider metadata such as Gemini thought signatures. ## Changes - Update the callback contract so streamed tokens can be either plain text or structured content blocks - Carry structured streamed content through tracing and event/log streaming paths without forcing provider data into text too early - Keep built-in text-oriented streaming callbacks working by converting structured tokens only at the display/queue boundary - Drop the now-incorrect `cast("str", ...)` on streamed content in `BaseChatModel` so the producer side matches the widened callback signature instead of asserting a string it doesn't always have (no runtime change — `cast` is erased) - Align Anthropic and Mistral content typing with the structured content shapes already used by chat model messages - Update callback tests to reflect that not every streamed value is text ## Compatibility No runtime behavior change: no producer emits anything it wasn't already emitting, and widening a parameter type is safe for existing callers and handlers that pass or receive `str`. The one caveat is downstream code that subclasses a callback handler or tracer and overrides `on_llm_new_token` with a `token: str` annotation — under strict type checking that override is now narrower than the base and will be flagged as incompatible with the supertype. Such code still runs unchanged; the fix is to widen the annotation to match.
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.