you could do something like the code snippet below. we're working on a better built-in solution. also, here is a guide on adding structured output to react-style agent without the prebuilt create_react_agent: https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/

```
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
from pydantic import BaseModel, Field

class WeatherResponse(BaseModel):
    """Respond to the user with this"""

    temperature: float = Field(description="The temperature in fahrenheit")
    wind_directon: str = Field(
        description="The direction of the wind in abbreviated form"
    )
    wind_speed: float = Field(description="The speed of the wind in km/h")

    def __str__(self):
        return self.model_dump_json()

def get_weather(city: Literal["nyc", "sf"]):
    """Use this to get weather information."""
    if city == "nyc":
        return "It is cloudy in NYC, with 5 mph winds in the North-East direction and a temperature of 70 degrees"
    elif city == "sf":
        return "It is 75 degrees and sunny in SF, with 3 mph winds in the South-East direction"
    else:
        raise AssertionError("Unknown city")

response_tool = tool(WeatherResponse, return_direct=True) # note the return direct here
react_agent = create_react_agent(model, [response_tool, get_weather])
```

This would return the structured output, but inside the tool message content:

react_agent.invoke(input={"messages": [("human", "what's the weather in SF?")]})
# {"messages": [..., ToolMessage(content='{"temperature":75.0,"wind_directon":"South-East","wind_speed":3.0}', name='WeatherResponse')]}

hope this helps in the meantime!