Quickstart

pip install openai

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.subconscious.dev/v1",
)

response = client.chat.completions.create(
    model="subconscious/tim-qwen3.6-27b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in three sentences."},
    ],
)

print(response.choices[0].message.content)

from openai import AsyncOpenAI
from agents import (
    Agent,
    Runner,
    OpenAIChatCompletionsModel,
    set_tracing_disabled,
)

API_KEY = "your-api-key"
MODEL = "subconscious/tim-qwen3.6-27b"


def run_openai_agent():
    client = AsyncOpenAI(
        api_key=API_KEY,
        base_url="https://api.subconscious.dev/v1",
    )
    # Tracing would otherwise try to reach OpenAI's backend.
    set_tracing_disabled(True)

    # Pass an explicit Chat Completions model bound to our client. A bare
    # model string would route through the MultiProvider, which treats the
    # "subconscious/" segment as a provider prefix and fails.
    agent = Agent(
        name="Assistant",
        instructions="You are a helpful assistant.",
        model=OpenAIChatCompletionsModel(model=MODEL, openai_client=client),
    )
    result = Runner.run_sync(agent, "What is the capital of France?")
    print("OpenAI Agents SDK response:")
    print(result.final_output)


run_openai_agent()

stream = client.chat.completions.create(
    model="subconscious/tim-qwen3.6-27b",
    messages=[{"role": "user", "content": "Write a haiku about programming."}],
    stream=True,
)

for chunk in stream:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

response = client.chat.completions.create(
    model="subconscious/tim-qwen3.6-27b",
    messages=[{"role": "user", "content": "Analyze the sentiment of: 'I love this product!'"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "sentiment_analysis",
            "schema": {
                "type": "object",
                "properties": {
                    "sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]},
                    "confidence": {"type": "number"},
                },
                "required": ["sentiment", "confidence"],
            },
        },
    },
)

import json
result = json.loads(response.choices[0].message.content)
print(result)  # {"sentiment": "positive", "confidence": 0.95}