mirror of
https://github.com/langchain-ai/langchain.git
synced 2026-10-05 01:15:09 +03:00
Josiah CoadandClaude Opus 5
4dd6f7101a
An MCP App is a tool that returns a user interface instead of text: it carries
a `ui://` resource in its `_meta.ui`, a host renders that in a sandboxed frame
beside the conversation, and the tool's result goes into it. The person moves
a slider and approves rather than reading a table and typing "yes, do that."
That makes a tool's audience part of its definition, and it puts one
obligation on anyone building an agent against such a server: a tool marked
`visibility: ["app"]` belongs to the app and MUST be kept out of the model's
tool list. The app exists so a person decides, and a model that can call the
submit tool can skip the person.
`langchain.mcp.apps` is that, and only that:
async with Client(url, extensions=[MCP_APPS_EXTENSION]) as client:
async with MCPAdapter(client) as adapter:
tools = filter_model_visible_tools(await adapter.list_tools())
Two details are easy to get backwards, and both fail silently rather than
loudly, which is why they belong in a library and not in every host:
ABSENT `visibility` MEANS BOTH AUDIENCES. It is absent on every tool on every
server that has never heard of this extension. Treating absent as "neither"
costs such a server half its tools, with nothing raised.
`_meta` MOVES WHEN A TOOL IS ADAPTED. An MCP `Tool` carries it on `.meta`; the
same tool after `MCPAdapter` keeps it under `metadata["mcp"]["tool"]["_meta"]`.
A host filters after adapting, so reading only the first shape hands the model
every app-only tool on the server. There is a test for exactly that path.
`MCP_APPS_EXTENSION` is what a host advertises during `initialize`. It is inert
against a server that always sends `_meta.ui` and load-bearing against one that
gates on the capability: the `ext-apps` SDK ships `getUiCapability` for that,
and its documented example registers a text-only tool for a client that did not
advertise, leaving a host with working tools and no apps.
Rendering the view, reading the `ui://` resource and proxying a view's own
`tools/call` are the browser-facing half and are not here.
13 tests; 91 pass in `tests/unit_tests/mcp`. ruff, format and mypy clean.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.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 capabilities 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