Maitai supports the use of JSON schemas or Pydantic models in order to get consistent JSON generation. This feature is available to all models that Maitai supports. 
import maitai

from pydantic import BaseModel

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

maitai_client = maitai.MaitaiAsync()
completion = await 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 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.MaitaiAsync()

response = await 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}
)