mirror of
https://github.com/hwchase17/langchain.git
synced 2026-07-01 06:42:37 +00:00
62f255980d
Enables mypy's `warn_unreachable` rule for `langchain-core`, bringing it
in line with the other strict libraries in the monorepo. Previously this
rule was intentionally disabled by a code comment, because under mypy
2.x it false-flags intentional defensive runtime checks — most notably
the SSRF / IP-policy guards in `langchain_core/_security/` — as
unreachable.
This PR resolves all of those warnings without deleting or
blanket-ignoring the defensive guards, so contributors get
unreachable-code coverage going forward and accidental dead code is
caught in CI.
The bulk of the change is mechanical: a targeted `# type:
ignore[unreachable]` on each defensive `else`/error branch that mypy
considers unreachable but that we deliberately keep as a runtime guard
against unexpected input. A few changes are more substantive and worth a
closer look:
- **`coro_with_context` (`runnables/utils.py`) — behavior change on
Python < 3.11.** The pre-3.11 path is rewritten to always route through
`context.run(asyncio.create_task, coro)`, so the supplied context is
reliably propagated to the task. Previously, on 3.10 the helper returned
the bare coroutine (run in the caller's context) when
`create_task=False`, and dropped the context entirely when
`create_task=True`. The new behavior matches 3.11+. The `create_task`
parameter is now inert but retained for signature compatibility. All
callers `await` the result, so returning a `Task` rather than a
coroutine is transparent.
- **`_create_template_from_message_type` (`prompts/chat.py`) — signature
widening.** This private helper's `template` parameter now accepts
`bool` inside the list, accurately reflecting the existing `["{var}",
is_optional]` placeholder form. No public-API impact.
- **`PydanticOutputFunctionsParser`
(`output_parsers/openai_functions.py`).** The `pydantic_schema` field is
typed as `TypeBaseModel` (which covers both v1 and v2 model classes,
unlike the prior annotation), and the `args_only` parse path now
dispatches explicitly on `BaseModel` vs `BaseModelV1` rather than
duck-typing via `hasattr`. This also yields clearer errors for
unsupported / dict schemas.
- **`_security/_policy.py`.** Loop variables are renamed so mypy can
narrow their types, which lets the old `# type: ignore[assignment]`
comments be dropped. The IP-blocklist logic is unchanged.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
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