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# Changes
## Adds support for `DynamicSystemPromptMiddleware`
```py
from langchain.agents.middleware import DynamicSystemPromptMiddleware
from langgraph.runtime import Runtime
from typing_extensions import TypedDict
class Context(TypedDict):
user_name: str
def system_prompt(state: AgentState, runtime: Runtime[Context]) -> str:
user_name = runtime.context.get("user_name", "n/a")
return f"You are a helpful assistant. Always address the user by their name: {user_name}"
middleware = DynamicSystemPromptMiddleware(system_prompt)
```
## Adds support for `runtime` in middleware hooks
```py
class AgentMiddleware(Generic[StateT, ContextT]):
def modify_model_request(
self,
request: ModelRequest,
state: StateT,
runtime: Runtime[ContextT], # Optional runtime parameter
) -> ModelRequest:
# upgrade model if runtime.context.subscription is `top-tier` or whatever
```
## Adds support for omitting state attributes from input / output
schemas
```py
from typing import Annotated, NotRequired
from langchain.agents.middleware.types import PrivateStateAttr, OmitFromInput, OmitFromOutput
class CustomState(AgentState):
# Private field - not in input or output schemas
internal_counter: NotRequired[Annotated[int, PrivateStateAttr]]
# Input-only field - not in output schema
user_input: NotRequired[Annotated[str, OmitFromOutput]]
# Output-only field - not in input schema
computed_result: NotRequired[Annotated[str, OmitFromInput]]
```
## Additionally
* Removes filtering of state before passing into middleware hooks
Typing is not foolproof here, still need to figure out some of the
generics stuff w/ state and context schema extensions for middleware.
TODO:
* More docs for middleware, should hold off on this until other prios
like MCP and deepagents are met
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building 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 -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
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.
LangChain’s 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:
- 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.
- 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.
- LangGraph Platform - 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 LangGraph Studio.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.
- Chat LangChain: Ask questions & chat with our documentation.