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`Runnable.__or__`, `Runnable.__ror__`, and their `RunnableSequence` and
`StructuredPrompt` overrides previously erased composition types: the
right-hand operand was typed `Runnable[Any, Other]`, so piping two
runnables together always produced `RunnableSerializable[Input, Any]`.
Type information was lost at every `|`, which is why chains so often
needed a `chain: Runnable = ...` annotation just to recover usable
inference.
This adds `@overload`s so the `Output` of one step flows into the
`Input` of the next and the composed result carries the real `Output`
type through. `Runnable[int, str] | Runnable[str, float]` now infers
`RunnableSerializable[int, float]` instead of `[int, Any]`.
`coerce_to_runnable` gains overloads so a `Mapping` resolves to
`RunnableParallel` while everything else stays a `Runnable`. As a
knock-on effect, dozens of now-unnecessary `: Runnable` annotations were
dropped from the test suite.
Runtime behavior is unchanged — this is a typing-only change.
## Impact on type-checked code
Most users will simply get better inference. Two changes can require a
small adjustment if you run a type checker (`mypy`, `pyright`):
### Stricter operand matching in `|`
The right-hand side of `|` is now typed `Runnable[Output, Other]` rather
than `Runnable[Any, Other]`, so the right operand's declared **input**
must match the left operand's **output**. This is more accurate, but it
surfaces a common pattern that was previously silent: piping a step that
outputs a plain `dict` into a step whose declared input is a more
specific type (for example a `TypedDict`). It still works at runtime;
the checker now reports an `[operator]` error.
If you hit this, narrow the boundary with a `cast` (or an explicit
annotation):
```python
from typing import Any, cast
from langchain_core.runnables import Runnable
# upstream outputs a dict; downstream declares a narrower input type
chain = cast("Runnable[Any, MyInput]", upstream) | downstream
```
### `list` → `Sequence` on `RunnableEach` / `map()`
`Runnable.map()` and the `invoke` / `ainvoke` methods of `RunnableEach`
now accept `Sequence[Input]` instead of `list[Input]`. Callers are
unaffected — a `list` is a `Sequence`, and tuples or other sequences now
type-check too. The only thing to adjust: if you **subclass**
`RunnableEach` (or `RunnableEachBase`) and override these methods with a
`list[...]` parameter, widen the annotation to `Sequence[...]` so the
override stays compatible with the base signature.
---------
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
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.