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a2e53fda73
Switches type checking for `langchain-text-splitters` from `mypy` to [`ty`](https://docs.astral.sh/ty/), which is much faster. The `ollama` package already [switched to `ty`](https://github.com/langchain-ai/langchain/pull/36571). ## What changed The core of this PR is the config swap (`[tool.mypy]` → `[tool.ty.rules]`/`[tool.ty.analysis]`, `Makefile`, and the `typing` dependency group). Because `ty` runs with `all = "error"`, a few modules also needed source-level adjustments to satisfy the stricter analysis. These are **behavior-preserving refactors** except for one intentional fix, called out below so reviewers know where to look. ### Behavioral change (intentional fix) - `SentenceTransformersTokenTextSplitter` now raises a clear `ValueError` when the underlying model reports no maximum sequence length **and** no `tokens_per_chunk` was provided. Previously this combination reached a `None > None` comparison and surfaced as an opaque `TypeError`. As a consequence, the public `maximum_tokens_per_chunk` attribute is now honestly typed as `int | None` — it can remain `None` when the caller supplies `tokens_per_chunk` explicitly for a model without a limit. ### Behavior-preserving refactors (no user-visible change) - `TokenTextSplitter.from_tiktoken_encoder` is now an explicit override rather than the base method dispatching on `issubclass(cls, TokenTextSplitter)`. The shared length-function logic moved into a private helper. Public signatures and return types are unchanged. - `NLTKTextSplitter` builds its tokenizer once at construction, so `_tokenizer` is now always a `Callable[[str], list[str]]`. The private attributes `_language` and `_use_span_tokenize` are no longer stored — flagging in case any downstream code read those (they are underscore-private). Tokenization output is unchanged. - `HTMLSemanticPreservingSplitter` text extraction was rewritten from a `cast`-based check to `isinstance(element, Tag)` narrowing; output is equivalent for tags, text nodes, and comments. --------- Co-authored-by: Mason Daugherty <github@mdrxy.com>
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
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.5")
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
Resources
- Documentation — conceptual overviews and guides
- LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
- API reference — complete reference for all public classes, functions, and types
- Discussions — community forum for technical questions, ideas, and feedback
- LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
- Contributing Guide — how to contribute and find good first issues
- Code of Conduct — community guidelines and standards