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Closes https://github.com/langchain-ai/langchain/issues/33956 * Making `ModelRequest` generic on `ContextT` and `ResponseT` so that we can thread type information through to `wrap_model_call` * Making builtin middlewares generic on `ContextT` and `ResponseT` so their context and response types can be inferred from the `create_agent` signature See new tests that verify backwards compatibility (for cases where folks use custom middleware that wasn't parametrized). This fixes: 1. Lack of access to context and response types in `wrap_model_call` 2. Lack of cohesion between middleware context + response types with those specified in `create_agent` See examples below: ### Type-safe context and response access ```python class MyMiddleware(AgentMiddleware[AgentState[AnalysisResult], UserContext, AnalysisResult]): def wrap_model_call( self, request: ModelRequest[UserContext], handler: Callable[[ModelRequest[UserContext]], ModelResponse[AnalysisResult]], ) -> ModelResponse[AnalysisResult]: # ✅ Now type-safe: IDE knows user_id exists and is str user_id: str = request.runtime.context["user_id"] # ❌ mypy error: "session_id" doesn't exist on UserContext request.runtime.context["session_id"] response = handler(request) if response.structured_response is not None: # ✅ Now type-safe: IDE knows sentiment exists and is str sentiment: str = response.structured_response.sentiment # ❌ mypy error: "summary" doesn't exist on AnalysisResult response.structured_response.summary return response ``` ### Mismatched middleware/schema caught at `create_agent` ```python class SessionMiddleware(AgentMiddleware[AgentState[Any], SessionContext, Any]): ... # ❌ mypy error: SessionMiddleware expects SessionContext, not UserContext create_agent( model=model, middleware=[SessionMiddleware()], context_schema=UserContext, # mismatch! ) class AnalysisMiddleware(AgentMiddleware[AgentState[AnalysisResult], ContextT, AnalysisResult]): ... # ❌ mypy error: AnalysisMiddleware expects AnalysisResult, not SummaryResult create_agent( model=model, middleware=[AnalysisMiddleware()], response_format=SummaryResult, # mismatch! ) ```
The platform for reliable agents.
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.
pip install langchain
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
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.
Note
Looking for the JS/TS library? Check out LangChain.js.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- 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.
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.
To improve your LLM application development, pair LangChain with:
- Deep Agents (new!) – Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph – Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows – and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- Integrations – List of LangChain integrations, including chat & embedding models, tools & toolkits, and more
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
Additional resources
- API Reference – Detailed reference on navigating base packages and integrations for LangChain.
- 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.