Structured Output

Adding a Structured Output

Now that you've seen how to add tools. Let's look at your agent can respond with reliable typed outputs. Schemas give you reliable, machine-checked outputs you can safely consume in code, rather than brittle strings.

class WeatherResponse(BaseModel):
    temperature: float
    condition: str

StructuredWeatherAgent = rt.agent_node(
    name="Weather Agent",
    llm=rt.llm.OpenAILLM("gpt-5.4-mini"),
    system_message="You are a helpful assistant that answers weather-related questions.",
    output_schema=WeatherResponse,
)

Pydantic: Defining a Schema

We use Pydantic to define structured data models.

from pydantic import BaseModel, Field

class YourModel(BaseModel):
    # Use the Field parameter for more control. 
    parameter: str = Field(default="default_value", description="Your description of the parameter")

Visit the pydantic docs to learn about what you can do with BaseModel's

Structured + Tool Calling

Often you will want the best of both worlds, an agent capable of both tool calling and responding in a structured format. At the time of writing this, most LLMs do not support structured output and tool calling together. In Railtracks we support the following two ways of achieving this:

1. Implement the Separation

Using a Flow you can separate out the tool calling and strucutred output steps. Here's an example how:

async def flow_func(arg: str)->str:
    first_resp = await rt.call(ToolCallingAgent, arg)

    second_resp = await rt.call(StructuredAgent, first_step.message_history) # or pass in first_step.content

ToolCallingAgent vs StructuredAgent

As noted in the first section of this page, the pure TooCallingAgent should not be passed an output_schema for things to work optimally this way, and similarly the StructuredAgent should not have tool_nodes passed to it.

2. Abstracted Separation

In this way, Railtracks automatically implements the steps above for you. Simply need provide the output_schema and tool_nodes parameter to the same agent_node definition.

WeatherToolCallAgent = rt.agent_node(
    name="Weather Agent",
    llm=rt.llm.OpenAILLM("gpt-5.4-mini"),
    system_message="You are a helpful assistant that answers weather-related questions.",
    tool_nodes=[weather_tool],
)

WeatherStructuredAgent = rt.agent_node(
    name="Weather Formatter Agent",
    llm=rt.llm.OpenAILLM("gpt-5.4-mini"),
    system_message="Extract the temperature and condition from the assistant's answer.",
    output_schema=WeatherResponse,
)

# Call the ToolCall agent first, then hand its answer to the structured agent, wrapped by an rt function
@rt.function_node
async def StructuredToolCallWeatherAgent(prompt: str):
    tool_response = await rt.call(WeatherToolCallAgent, user_input=prompt)
    return await rt.call(WeatherStructuredAgent, user_input=tool_response.text)