Structured Output

Reka Research supports structured output, so you can define exactly how responses should be formatted. Use a JSON Schema to ensure responses are always parseable, typed, and ready for downstream use — no fragile post-processing required.

Structured outputs are useful for applications requiring predictable output, such as:

• Enforcing contracts between systems
• Validating results automatically
• Integrating seamlessly with other systems

Supported format

Structured output is supported via JSON Schema. Use the response_format field to specify your expected structure.

JSON Schema

Define the fields and types your response must follow — the model will only return content that matches.

The schema must be a valid JSON Schema object.

How to use

We recommend using the OpenAI SDK helper to parse the agent’s output into your desired format.

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import os
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from pydantic import BaseModel
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from openai import OpenAI
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client = OpenAI(
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    base_url="https://api.reka.ai/v1",
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    api_key=os.getenv("REKA_API_KEY")
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)
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class AnswerFormat(BaseModel):
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    country: str
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    date: str
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completion = client.chat.completions.parse(
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    model="reka-flash-research",
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    messages=[
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        {
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            "role": "user",
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            "content": "Who won the UEFA Nations League 2025?",
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        },
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    ],
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    response_format=AnswerFormat,
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)
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print(completion.choices[0].message.parsed)

What you get back

$
country='Portugal' date='2025-06-09'

Tips and best practices

• Be explicit — include a short note in your prompt that describes the expected structure. It helps the model stay on track and improves reliability.
• Mistakes can still happen. If the output isn’t what you expect, refine your instructions, break down the task, or include structured examples.
• Use Pydantic in Python to define your schema. It’s the easiest way to produce a valid JSON Schema from a class. Learn more.