MegaNova Python SDK

Official Python SDK for MegaNova AI — inference, cloud agents, and agent SDK.

Also available: JavaScript/TypeScript SDK (npm install meganova)

Installation

pip install meganova

Requires Python 3.9+.

Configuration

The SDK defaults to the production API URL: https://api.meganova.ai/v1.

You can configure the client using your API key:

from meganova import MegaNova

client = MegaNova(api_key="YOUR_API_KEY")

Alternatively, set the MEGANOVA_API_KEY environment variable.

Supported Models

Meganova supports a wide range of models across different modalities:

• Text Generation:
  • meganova-ai/manta-mini-1.0 (Fast, 8k context)
  • meganova-ai/manta-flash-1.0 (Balanced, 32k context)
  • meganova-ai/manta-pro-1.0 (Powerful, 65k context)
  • Qwen/Qwen3-235B-A22B-Instruct-2507 (262k context)
  • moonshotai/Kimi-K2-Thinking (Reasoning focused)
• Image Generation:
  • ByteDance/SeedDream-4-5 (Text-to-Image)
  • black-forest-labs/FLUX.1-dev (Text-to-Image)
• Video Generation:
  • Byteplus/seedance-1-0-pro-250528 (Text-to-Video)
• Vision:
  • Qwen/Qwen2.5-VL-7B-Instruct
• Audio:
  • Systran/faster-whisper-large-v3 (Speech-to-Text)

Use client.models.list() or client.serverless.list_models() to see the full list of available models, capabilities, and pricing.

Cloud Agents

Chat with deployed MegaNova Cloud agents using just an API key — no authentication setup required.

Quick Start

from meganova.cloud import CloudAgent

agent = CloudAgent(api_key="agent_xxx...")

# Get agent info
info = agent.info()
print(f"{info.name}: {info.welcome_message}")

# Chat
response = agent.chat("Hello!")
print(response.response)

Multi-turn Conversation

conv = agent.conversation()
r1 = conv.chat("I need help with my account")
r2 = conv.chat("My email is user@example.com")
# conversation_id is tracked automatically

Tool Confirmation

Some agents use tools (ticket creation, email, etc.) that require approval:

conv = agent.conversation()
response = conv.chat("Create a support ticket for my login issue")

if conv.pending_tool_call:
    print(f"Agent wants to: {conv.pending_tool_call.description}")
    result = conv.confirm()   # or conv.reject()

OpenAI-compatible Completions

# Non-streaming
response = agent.completions(
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

# Streaming
for chunk in agent.completions(
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True,
):
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

See examples/studio_agent_*.py for complete runnable examples.

Usage Examples

1. Basic Chat Completion

from meganova import MegaNova

client = MegaNova(api_key="YOUR_API_KEY")

response = client.chat.completions.create(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ],
    model="meganova-ai/manta-flash-1.0",
    temperature=0.7
)

print(response.choices[0].message.content)
print(f"Usage: {response.usage}")

2. Streaming Chat

response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Write a haiku about space."}],
    model="meganova-ai/manta-flash-1.0",
    stream=True
)

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

3. Listing Models

models = client.models.list()
for model in models:
    print(f"ID: {model.id}")
    print(f"  Name: {model.name}")
    print(f"  Context: {model.context_length}")
    print(f"  Tags: {model.tags}")

4. Serverless Model Discovery

Browse available serverless models with pricing information:

# List text generation models
result = client.serverless.list_models(modality="text_generation")
print(f"Found {result.count} text models")
for model in result.models:
    print(f"  {model.model_name} ({model.model_alias})")
    print(f"    Input: ${model.cost_per_1k_input}/1K tokens")
    print(f"    Output: ${model.cost_per_1k_output}/1K tokens")

# List image generation models
result = client.serverless.list_models(modality="text_to_image")
print(f"Found {result.count} image models")
for model in result.models:
    print(f"  {model.model_name}")

5. Image Generation

Generate images from text prompts:

response = client.images.generate(
    prompt="A futuristic city skyline at sunset, digital art",
    model="ByteDance/SeedDream-4-5",
    width=1024,
    height=1024,
    num_steps=4,
    guidance_scale=3.5,
)

# Response contains base64-encoded image(s)
for image in response.data:
    import base64
    image_bytes = base64.b64decode(image.b64_json)
    with open("output.png", "wb") as f:
        f.write(image_bytes)
    print("Image saved to output.png")

6. Audio Transcription

Transcribe audio files to text:

# From a file path
result = client.audio.transcribe("recording.mp3")
print(result.text)

# From a file object
with open("recording.mp3", "rb") as f:
    result = client.audio.transcribe(f, filename="recording.mp3")
    print(result.text)

# With a specific model
result = client.audio.transcribe(
    "recording.mp3",
    model="Systran/faster-whisper-large-v3",
)
print(result.text)

7. Usage & Billing

# Get usage summary
usage = client.usage.summary(start_date="2024-01-01", end_date="2024-01-31")
print(f"Total Requests: {usage.total_requests}")

# Get billing balance
balance = client.billing.get_balance()
print(f"Current Balance: ${balance.amount} {balance.currency}")

Running Examples

The repository includes ready-to-run examples in the examples/ directory.

1. Create a .env file in the root directory:

   MEGANOVA_API_KEY=your_api_key_here
   STUDIO_AGENT_KEY=agent_xxx...  # Optional, for Cloud agent examples
   

2. Run an example:

   # Inference API examples
   python examples/basic_chat.py
   python examples/streaming_chat.py
   python examples/list_models.py
   python examples/serverless_models.py
   python examples/image_generation.py
   python examples/audio_transcription.py path/to/audio.mp3
   python examples/usage_billing.py
   
   # Cloud Agent examples
   python examples/studio_agent_chat.py
   python examples/studio_agent_streaming.py
   python examples/studio_agent_tools.py
   

Development & Testing

Context Length Verification

A utility script is provided to verify the actual enforced context length of a model versus its claimed metadata.

python examples/test_context_length.py meganova-ai/manta-flash-1.0

SDKs

Language	Package	Install
Python	meganova	pip install meganova
JavaScript/TypeScript	meganova	npm install meganova

Both SDKs share the same API surface: MegaNova (inference) + CloudAgent (deployed Studio agents).

Features

• Chat Completions: Full support for streaming and non-streaming requests.
• Cloud Agents: Chat with deployed Cloud agents via simple API key — multi-turn conversations, tool confirmation, and OpenAI-compatible completions.
• Model Management: List and retrieve details for all available models.
• Serverless Model Discovery: Browse models by modality with pricing info.
• Image Generation: Generate images from text prompts with configurable parameters.
• Audio Transcription: Transcribe audio files to text (supports file paths and file objects).
• Usage & Billing: Monitor your usage and check balances programmatically.
• Type Safety: Fully typed with Pydantic models for excellent IDE support.
• Robustness: Built-in retry logic (exponential backoff) and error handling.