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John | Elite Encoder edited this page Aug 25, 2025 · 3 revisions

Welcome to the PyTrickle wiki — your central hub for building AI-powered video, audio, and data streaming applications on the Livepeer network.

PyTrickle provides a modular, async-first Python SDK and a flexible streaming server that make it easy to process, transform, and publish real-time media with minimal boilerplate.

🚀 What is PyTrickle?

PyTrickle is designed for developers who need:

  • Frame-based processing for video and audio
  • Asynchronous pipelines for maximum throughput
  • Protocol abstraction via the Trickle protocol
  • Built-in REST APIs for control and parameter updates
  • Real-time structured data publishing alongside media streams

Whether you’re building a computer vision pipeline, an audio effects processor, or a multi-modal analytics service, PyTrickle gives you the building blocks to go from prototype to production.

📚 Documentation Index

1. PyTrickle Core SDK Classes

Learn about the core classes that power PyTrickle:

  • StreamProcessor — the high-level, function-based interface for rapid development
  • FrameProcessor — the advanced, subclass-based interface for fine-grained control
  • Media frame abstractions (VideoFrame, AudioFrame)
  • Protocol integration and data publishing

2. StreamServer API Reference

Explore the full REST API for managing streaming operations:

  • Start, stop, and control streams
  • Real-time parameter updates
  • Status monitoring and performance metrics
  • Health checks, GPU stats, and custom routes

🛠 Example Use Cases

  • AI Video Processing — Apply ML models to live video streams
  • Audio Transformation — Real-time effects, filtering, and analysis
  • Multi-modal Analytics — Publish structured data alongside media
  • Low-latency Streaming — Build interactive, event-driven pipelines

📦 Getting Started

  1. Install PyTrickle from source or PyPI (coming soon)
  2. Choose your interface:
    • Quick start with StreamProcessor
    • Advanced control with FrameProcessor + StreamServer
  3. Run your processor and connect it to your Livepeer streams

🔮 Roadmap

  • More examples and tutorials
  • Expanded API endpoints
  • Deployment guides for Docker/Kubernetes
  • Integration patterns for popular AI frameworks

💡 Tip: If you’re new here, start with the PyTrickle Core SDK Classes page to understand the building blocks, then dive into the StreamServer API Reference to see how to control your pipelines in real time.

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