buble-ai 0.1.0

Official Python SDK for the Buble AI media generation, app workflow, and chat API.

buble-ai

Official Python SDK for the Buble AI public API. Use it from server-side Python code to create AI image and video generation tasks, run preconfigured Buble apps, upload source media, and call Buble chat models through OpenAI, Anthropic, or Gemini-compatible endpoints.

buble-ai is not related to the existing buble package on PyPI.

Keep your API key on the server. Do not expose BUBLE_API_KEY in browser or client-side code.

Installation

pip install buble-ai

Quick Start

from buble_ai import Buble

client = Buble(api_key="sk_...")

task = client.generations.create(
    model="google/nano-banana",
    mode="text_to_image",
    prompt="A cinematic product photo of a matte black espresso cup",
    aspect_ratio="1:1",
    output_format="png",
)

result = client.generations.wait(task["data"]["id"])
print(result["data"]["result"]["images"][0]["url"])

The client reads BUBLE_API_KEY and BUBLE_BASE_URL from the environment when omitted.

Discover Media Models

models = client.media_models.list(media_type="video")

for model in models["data"]:
    print(model["model"], [op["mode"] for op in model.get("operations", [])])

Use media_models.list() as the source of truth for model keys, modes, required inputs, and public parameters.

Upload Files

uploaded = client.files.upload(
    "reference.png",
    file_type="image",
    model="google/nano-banana",
    mode="image_to_image",
)

task = client.generations.create(
    model="google/nano-banana",
    mode="image_to_image",
    prompt="Turn this reference into a polished ecommerce hero image.",
    image_urls=[uploaded["data"]["url"]],
)

Uploads support paths, bytes, bytearray, and binary file objects. Paths are streamed from disk instead of being read fully into memory.

Video Generation

task = client.generations.create(
    model="doubao/seedance-2.0-fast",
    mode="text_to_video",
    prompt="A cinematic wide shot of a futuristic train station at sunrise.",
    duration="8s",
    resolution="720p",
    aspect_ratio="16:9",
)

result = client.generations.wait(task["data"]["id"], interval=2.0, timeout=600.0)
print(result["data"]["result"]["videos"][0]["url"])

Generation request bodies are flat JSON. Do not send internal Buble fields such as input, options, scene, sub_mode_id, provider, mediaType, or media_type.

Apps

app = client.apps.retrieve("video-background-remover")
print(app["data"]["input_parameters"])

task = client.apps.generations.create(
    "video-background-remover",
    source_video=["https://example.com/source.mp4"],
    refine_foreground_edges=True,
    subject_is_person=True,
)

result = client.apps.generations.wait("video-background-remover", task["data"]["id"])
print(result["data"]["result"]["videos"][0]["url"])

Apps are preconfigured workflows. Only send parameter names returned by apps.list() or apps.retrieve().

Chat

OpenAI-Compatible

completion = client.chat.completions.create(
    model="openai/gpt-5.5",
    messages=[{"role": "user", "content": "Write a short launch summary."}],
    reasoning=True,
    max_completion_tokens=800,
)

print(completion["choices"][0]["message"]["content"])

Streaming

for text in client.chat.completions.stream_text(
    model="openai/gpt-5.5",
    messages=[{"role": "user", "content": "Write one sentence at a time."}],
):
    print(text, end="")

Anthropic-Compatible

message = client.chat.messages.create(
    model="openai/gpt-5.5",
    system="You are concise.",
    messages=[{"role": "user", "content": "Summarize this release."}],
    max_tokens=800,
)

Gemini-Compatible

response = client.chat.gemini.generate_content(
    "openai/gpt-5.5",
    contents=[
        {
            "role": "user",
            "parts": [{"text": "Write a short launch summary."}],
        }
    ],
)

Gemini streaming uses stream_generate_content, not stream=True on generate_content.

Async Client

from buble_ai import AsyncBuble

async with AsyncBuble() as client:
    models = await client.chat.models.list()

Error Handling

from buble_ai import BubleAPIError, BubleGenerationError

try:
    client.generations.create(model="missing/model", mode="text_to_image")
except BubleAPIError as error:
    print(error.status_code, error.code, error.message, error.details)

try:
    client.generations.wait("task_id")
except BubleGenerationError as error:
    print(error.task)

Development

python -m pip install -e ".[dev]"
pytest
python -m build
python -m twine check dist/*

Live smoke test:

BUBLE_API_KEY=sk_... python scripts/live_smoke.py

The live smoke test does not create billable generation tasks.