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89079ad411
# Main Changes
1. Adding decorator utilities for dynamically defining middleware with
single hook functions (see an example below for dynamic system prompt)
2. Adding better conditional edge drawing with jump configuration
attached to middleware. Can be registered w/ the decorator new
decorator!
## Decorator Utilities
```py
from langchain.agents.middleware_agent import create_agent, AgentState, ModelRequest
from langchain.agents.middleware.types import modify_model_request
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import InMemorySaver
@modify_model_request
def modify_system_prompt(request: ModelRequest, state: AgentState) -> ModelRequest:
request.system_prompt = (
"You are a helpful assistant."
f"Please record the number of previous messages in your response: {len(state['messages'])}"
)
return request
agent = create_agent(
model="openai:gpt-4o-mini",
middleware=[modify_system_prompt]
).compile(checkpointer=InMemorySaver())
```
## Visualization and Routing improvements
We now require that middlewares define the valid jumps for each hook.
If using the new decorator syntax, this can be done with:
```py
@before_model(jump_to=["__end__"])
@after_model(jump_to=["tools", "__end__"])
```
If using the subclassing syntax, you can use these two class vars:
```py
class MyMiddlewareAgentMiddleware):
before_model_jump_to = ["__end__"]
after_model_jump_to = ["tools", "__end__"]
```
Open for debate if we want to bundle these in a single jump map / config
for a middleware. Easy to migrate later if we decide to add more hooks.
We will need to **really clearly document** that these must be
explicitly set in order to enable conditional edges.
Notice for the below case, `Middleware2` does actually enable jumps.
<table>
<thead>
<tr>
<th>Before (broken), adding conditional edges unconditionally</th>
<th>After (fixed), adding conditional edges sparingly</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<img width="619" height="508" alt="Screenshot 2025-09-23 at 10 23 23 AM"
src="https://github.com/user-attachments/assets/bba2d098-a839-4335-8e8c-b50dd8090959"
/>
</td>
<td>
<img width="469" height="490" alt="Screenshot 2025-09-23 at 10 23 13 AM"
src="https://github.com/user-attachments/assets/717abf0b-fc73-4d5f-9313-b81247d8fe26"
/>
</td>
</tr>
</tbody>
</table>
<details>
<summary>Snippet for the above</summary>
```py
from typing import Any
from langchain.agents.tool_node import InjectedState
from langgraph.runtime import Runtime
from langchain.agents.middleware.types import AgentMiddleware, AgentState
from langchain.agents.middleware_agent import create_agent
from langchain_core.tools import tool
from typing import Annotated
from langchain_core.messages import HumanMessage
from typing_extensions import NotRequired
@tool
def simple_tool(input: str) -> str:
"""A simple tool."""
return "successful tool call"
class Middleware1(AgentMiddleware):
"""Custom middleware that adds a simple tool."""
tools = [simple_tool]
def before_model(self, state: AgentState, runtime: Runtime) -> None:
return None
def after_model(self, state: AgentState, runtime: Runtime) -> None:
return None
class Middleware2(AgentMiddleware):
before_model_jump_to = ["tools", "__end__"]
def before_model(self, state: AgentState, runtime: Runtime) -> None:
return None
def after_model(self, state: AgentState, runtime: Runtime) -> None:
return None
class Middleware3(AgentMiddleware):
def before_model(self, state: AgentState, runtime: Runtime) -> None:
return None
def after_model(self, state: AgentState, runtime: Runtime) -> None:
return None
builder = create_agent(
model="openai:gpt-4o-mini",
middleware=[Middleware1(), Middleware2(), Middleware3()],
system_prompt="You are a helpful assistant.",
)
agent = builder.compile()
```
</details>
## More Examples
### Guardrails `after_model`
<img width="379" height="335" alt="Screenshot 2025-09-23 at 10 40 09 AM"
src="https://github.com/user-attachments/assets/45bac7dd-398e-45d1-ae58-6ecfa27dfc87"
/>
<details>
<summary>Code</summary>
```py
from langchain.agents.middleware_agent import create_agent, AgentState, ModelRequest
from langchain.agents.middleware.types import after_model
from langchain_core.messages import HumanMessage, AIMessage
from langgraph.checkpoint.memory import InMemorySaver
from typing import cast, Any
@after_model(jump_to=["model", "__end__"])
def after_model_hook(state: AgentState) -> dict[str, Any]:
"""Check the last AI message for safety violations."""
last_message_content = cast(AIMessage, state["messages"][-1]).content.lower()
print(last_message_content)
unsafe_keywords = ["pineapple"]
if any(keyword in last_message_content for keyword in unsafe_keywords):
# Jump back to model to regenerate response
return {"jump_to": "model", "messages": [HumanMessage("Please regenerate your response, and don't talk about pineapples. You can talk about apples instead.")]}
return {"jump_to": "__end__"}
# Create agent with guardrails middleware
agent = create_agent(
model="openai:gpt-4o-mini",
middleware=[after_model_hook],
system_prompt="Keep your responses to one sentence please!"
).compile()
# Test with potentially unsafe input
result = agent.invoke(
{"messages": [HumanMessage("Tell me something about pineapples")]},
)
for msg in result["messages"]:
print(msg.pretty_print())
"""
================================ Human Message =================================
Tell me something about pineapples
None
================================== Ai Message ==================================
Pineapples are tropical fruits known for their sweet, tangy flavor and distinctive spiky exterior.
None
================================ Human Message =================================
Please regenerate your response, and don't talk about pineapples. You can talk about apples instead.
None
================================== Ai Message ==================================
Apples are popular fruits that come in various varieties, known for their crisp texture and sweetness, and are often used in cooking and baking.
None
"""
```
</details>
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.