StrapTo Local Server

Disclaimer: This repository is currently under active development and not all features are available yet.

StrapTo is the provider-side component of the StrapTo suite, designed to capture and stream outputs from self-hosted AI models (e.g., Ollama, LMStudio, Llama.cpp, LangChain/Langgraph, etc.) in real time. Built in Python with the aiortc library, the local server handles low-latency WebRTC connections and facilitates bi-directional communication—allowing consumer inputs to be fed back into your model.

Overview

The StrapTo Local Server enables model providers to:

• Capture diverse outputs (text, JSON, images, etc.) from self-hosted AI models.
• Stream these outputs in real time to remote consumers using WebRTC.
• Receive interactive input from consumers, which can be routed back into the model.
• Manage the streaming session with controls for starting, pausing/resuming, and stopping the stream.

This repository focuses solely on the local server component. For a complete interactive streaming experience, see the other StrapTo repositories:

• StrapTo Host: Manages room creation, WebRTC signaling, and real-time communication (built with Node.js, Express, Socket.io, and PeerJS).
• StrapTo Client: A modern React-based interface for both model providers and consumers.

Features

• Real-Time Streaming:
  Transmit any type of model output (plain text, formatted JSON, images, etc.) with low latency.

• Bi-Directional Communication:
  Capture consumer messages (chat, suggestions, votes) and relay them to the local model, enabling interactive sessions.

• Flexible Integration:
  Designed to plug into a variety of self-hosted AI model frameworks, making it easy to adapt to your existing setup.

• Provider Control:
  Offers session management functions to start, pause/resume, and stop the stream, giving you full control over the interactive experience.

• Modular Architecture:
  Operates as a standalone Python service that seamlessly integrates with other StrapTo components to form a complete interactive streaming solution.

Architecture Overview

The StrapTo Local Server is built in Python using aiortc to handle the real-time aspects of WebRTC communications. Its core responsibilities include:

• Capturing Model Outputs:
  Interfacing directly with your self-hosted AI model to capture and stream outputs in real time.

• Establishing WebRTC Connections:
  Setting up and maintaining peer-to-peer data channels for efficient, low-latency transmission of model outputs to connected consumers.

• Routing Consumer Inputs:
  Receiving bi-directional messages from consumers via established signaling channels and relaying these inputs back into the model. This enables interactive, two-way sessions where feedback can influence the model’s behavior.

• Interoperability with Other Components:
  While this repository focuses on the local server, it is designed to work in concert with:

  • A dedicated signaling server that handles room management and the WebRTC handshake.
  • A React-based frontend that provides an interactive interface for both the model provider and end users.

Use Cases

• Live Model Streaming:
  Broadcast live outputs from your local AI model to an audience, ideal for demonstrations, interactive sessions, or remote collaboration.

• Interactive Feedback Loop:
  Enable real-time consumer interaction where inputs like chat messages or suggestions can directly influence ongoing model outputs.

• Modular Integration:
  Seamlessly integrate into various self-hosted AI workflows, providing a plug-and-play solution for real-time streaming and interaction.

Getting Started

Coming soon...