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PipeCat is an open-source Python framework that handles complex orchestration of AI services, network transport, audio processing, and multimodal interactions. Learn to use the SambaNova LLM integration and try the example use cases for building real-time speech applications.

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Prerequisites

Before starting, ensure you have:
  • A SambaCloud account and API key.
  • Ensure Python version 3.11 or 3.12 is installed locally.
  • A PipeCat-compatible STT provider key (e.g., Cartesia).
  • .env file with all your API keys in your working directory.

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Install dependencies

pip install python-dotenv fastapi uvicorn
pip install pipecat-ai-small-webrtc-prebuilt
pip install pipecat-ai[sambanova, webrtc, silero, daily]

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Usage

You can integrate SambaNova’s LLM services into your Pipecat pipeline as shown below: 
from pipecat.services.sambanova.llm import SambaNovaLLMService
from pipecat.transcriptions.language import Language
from pipecat.pipeline.pipeline import Pipeline

# Instantiate SambaNova services
sambanova_llm = SambaNovaLLMService(
    api_key='your-sambanova-api-key',
    model='Llama-4-Maverick-17B-128E-Instruct',
    params=SambaNovaLLMService.InputParams(
        temperature=0.7,
        max_tokens=1024
    )
)

# Add the SambaNova models to your pipeline
pipeline = Pipeline([
    transport.input(),
    stt,
    ...
    sambanova_llm,
    tts,
    transport.output(),
    ...
])
Follow the integrations example to build and deploy a real-time speech-to-speech weather agent.

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Additional resources