nineth 0.1.3

model sdk built by the 9th ditrict at tooig

nineth

nineth is the Python SDK for the 1984 model API, built by the 9th District at Tooig.

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Install

pip install nineth
export NINETH_API_KEY="your-api-key"

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How it works

Every request goes through client.model.request(...).

• Pass a task. Get a response.
• Set stream=True to receive text as it arrives, word by word.
• Everything else — caching, service routing, continuity — is handled server-side.

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Models

Name	Description
1984-m3-0317	Most capable. Best for research and complex tasks.
1984-m2-preview	Fast and powerful. Good for most tasks.
1984-m2-light	Lightweight, quick general tasks.
1984-m1-unified	High-throughput unified model.
1984-m0-brute	Compact efficient model.
1984-m0-sm	Smallest model, fastest responses.

Set a default at client creation or pass model= per call.

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Cookbook

1 — Get a response

The simplest case. Ask something, get the answer.

from nineth import NinethClient

with NinethClient(default_model="1984-m3-0317") as client:
    response = client.model.request("Give me a tight BTC market brief.")
    print(response["final_response"])

response is a plain dict. The text is always in response["final_response"].

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2 — Stream the response live

Set stream=True to print text as it arrives.

from nineth import NinethClient

with NinethClient(default_model="1984-m3-0317") as client:
    for event in client.model.request("Summarise crude oil today.", stream=True):
        if event["type"] == "model_delta":
            print(event["data"]["text"], end="", flush=True)

The last event in the stream is type: result and contains the full final_response alongside iterations.

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3 — Choose a different model per request

from nineth import NinethClient

with NinethClient() as client:
    response = client.model.request(
        "What happened with Nvidia earnings?",
        model="1984-m2-light",
    )
    print(response["final_response"])

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4 — Control reasoning depth

Use reasoning to hint at how deeply the model should think before answering. Valid values: "low", "medium", "high". Leave it out to use the model default.

from nineth import NinethClient

with NinethClient(default_model="1984-m3-0317") as client:
    response = client.model.request(
        "Analyse the macro impact of a Fed rate pause.",
        reasoning="high",
    )
    print(response["final_response"])

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5 — Show the model's reasoning

Set show_reasoning=True to include the model's internal chain-of-thought. This is off by default.

from nineth import NinethClient

with NinethClient(default_model="1984-m3-0317") as client:
    response = client.model.request(
        "Walk me through whether gold is trending or ranging.",
        reasoning="medium",
        show_reasoning=True,
    )
    for block in response.get("thinking", []):
        print("[thinking]", block)
    print(response["final_response"])

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6 — Limit how many turns the model takes

max_iterations controls how many model turns the server runs. The default is 10. Most tasks finish in 1–3 turns.

from nineth import NinethClient

with NinethClient(default_model="1984-m3-0317") as client:
    response = client.model.request(
        "Give me a one-paragraph ETH brief.",
        max_iterations=2,
    )
    print(response["final_response"])

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7 — Async usage

import asyncio
from nineth import AsyncNinethClient

async def main():
    async with AsyncNinethClient(default_model="1984-m3-0317") as client:
        response = await client.model.request(
            "Summarise macro risk factors this week.",
        )
        print(response["final_response"])

asyncio.run(main())

Async streaming works the same way:

import asyncio
from nineth import AsyncNinethClient

async def main():
    async with AsyncNinethClient(default_model="1984-m3-0317") as client:
        async for event in await client.model.request(
            "Research BTC ETF flows.", stream=True
        ):
            if event["type"] == "model_delta":
                print(event["data"]["text"], end="", flush=True)

asyncio.run(main())

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8 — Health check

No API key needed. Use this to verify the endpoint is reachable.

from nineth import NinethClient

with NinethClient() as client:
    print(client.health())
# {'status': 'ok', 'timestamp': '2026-04-04T00:00:00+00:00'}

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9 — Point the SDK at a different endpoint

from nineth import NinethClient

with NinethClient(
    base_url="https://your-deployment.modal.run",
    api_key="your-key",
    default_model="1984-m3-0317",
) as client:
    response = client.model.request("Hello.")
    print(response["final_response"])

Or use environment variables:

export NINETH_BASE_URL="https://your-deployment.modal.run"
export NINETH_MODEL="1984-m3-0317"

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Response shape

Buffered (stream=False)

{
    "final_response": "Bitcoin is trading near...",
    "iterations": 2,
    "usage": {"prompt_tokens": 412, "completion_tokens": 88, "total_tokens": 500},
    "thinking": [],          # only populated when show_reasoning=True
    "service_calls": [...],
    "service_responses": [...],
    "events": [...],
}

Only final_response and iterations are guaranteed to be present on every response.

Streaming (stream=True)

Each loop iteration yields a dict:

# Text arriving live
{"type": "model_delta", "data": {"text": "Bitcoin is trading..."}}

# Tool calls the model made
{"type": "service_call",     "data": {"service_name": "search_web", "params": {...}}}
{"type": "service_response", "data": {"service_name": "search_web", "success": True, "summary": {...}}}

# Final summary — always the last event
{"type": "result", "data": {"final_response": "...", "iterations": 2}}

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Error handling

from nineth import NinethClient, NinethAPIError

with NinethClient(default_model="1984-m3-0317") as client:
    try:
        response = client.model.request("Analyse ETH.")
    except NinethAPIError as exc:
        print("API error:", exc)
    except ValueError as exc:
        print("Configuration error:", exc)

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Authentication

Set NINETH_API_KEY in your environment or pass api_key= to the client constructor. The health check endpoint does not require a key.