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Maitai supports JSON Schema-based structured output. In Python, you can also pass a Pydantic model and parse the result via message.parsed. 
import maitai

from pydantic import BaseModel

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

maitai_client = maitai.Maitai()
completion = maitai_client.chat.completions.create(
    application="DEMO_APP",
    intent="CALENDAR",
    model="llama3.1-8b",
    messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "AI learning seminar, October 20."},
    ],
    response_format=CalendarEvent,
)

event: CalendarEvent = completion.choices[0].message.parsed

​
JSON Schema example

import maitai

schema = {
    "name": "athlete_schema",
    "strict": True,
    "schema": {
        "type": "object",
        "properties": {
            "athletes": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "sport": {"type": "string"},
                        "name": {"type": "string"},
                    },
                    "required": ["sport", "name"],
                    "additionalProperties": False,
                },
            }
        },
        "required": ["athletes"],
        "additionalProperties": False,
    },
}

maitai_client = maitai.Maitai()

response = maitai_client.chat.completions.create(
    session_id="YOUR_SESSION_ID",
    intent="FACTS",
    application="DEMO_APP",
    model="claude-3-5-sonnet-20240620",
    messages=[{"role": "user", "content": "Give me a list of 5 famous athletes and their sport"}],
    response_format={"type": "json_schema", "json_schema": schema}
)

data = response.choices[0].message.parsed