mittal-ai

Mittal Analytics' reusable AI harness. Install it as mittal-ai and import it as mittal_ai. It provides:

• streaming, non-streaming and structured LLM responses;
• tool-call handling and message-history repair;
• token-cost calculation;
• OpenRouter routing and provider preferences;
• compatibility fixes for Chinese models; and
• dj-evals events for model requests and tool calls.

The application keeps its API keys. The model declares which provider and base URL the harness should use:

from mittal_ai import AIModel, get_client, get_structured_response
from pydantic import BaseModel


class Summary(BaseModel):
    text: str


model = AIModel(
    name="openai/gpt-5.4",
    api_key="...",
    provider="openrouter",
    base_url="https://openrouter.ai/api/v1",
    input_tokens_cost_usd=2.5,
    input_tokens_cached_cost_usd=0.25,
    output_tokens_cost_usd=15,
    output_tokens_reasoning_cost_usd=15,
)

async with get_client(model) as client:
    async for event in get_structured_response(
        client=client,
        ai_model=model,
        input=[{"role": "user", "content": "Summarise this."}],
        tools=[],
        text_format=Summary,
        reasoning_effort="low",
    ):
        print(event)

The main public entry points are get_response, get_streaming_response and get_structured_response.