The set of software boundaries, interaction surfaces, and protocol contracts through which humans, AI agents, and system components communicate. In the context of LLMs and spatial computing, interfaces include node-based visual editors, chat frontends, API gateways, and multimodal input layers that mediate access to underlying AI or infrastructure capabilities.

Semantic Classification

Content

Node based

LLM and multimodal local

  • Description: Web-based UI inspired by ChatGPT, designed for high extensibility.
  • Features:
    • Workspaces for personalised assistants (similar to GPT’s custom setups).
    • OpenAI-compatible endpoints for streamlined backend integration.
    • Optimised for responsiveness, especially on touchscreen devices.
  • Use Cases: General-purpose use, roleplay (RP), and advanced configuration.
  • Limitations: Lack of comprehensive documentation remains a significant barrier.
  • Link: Open WebUI GitHub
  • Description: Web-based interface focusing on customisation and versatility.
  • Features:
    • Presets for reusable configurations.
    • YAML and .env support for endpoint and key management.
  • Limitations:
    • Export/migration tools are cumbersome.
    • Slower responsiveness compared to Open WebUI.
  • Link: LibreChat GitHub
  • Description: Universal adapter popular for roleplay.
  • Features:
    • Wide compatibility with backends like Koboldcpp.
    • Growing focus on general use beyond RP.
  • Limitations: Outdated UI design limits appeal.
  • Link: SillyTavern GitHub
  • Description: Standalone desktop application for local inference.
  • Features:
    • Easy to use with a modern UI.
    • Suitable for new users and traditional “Windows-style” workflows.
  • Limitations: Closed source; outbound connections for updates raise privacy concerns.
  • Link: LM Studio
  • Description: Native macOS application with extensive feature sets.
  • Features:
    • Integrates advanced tools like text-to-speech (TTS).
    • Highly optimised for macOS environments.
  • Link: Msty App
  • Description: Lightweight Android app for local models.
  • Features:
    • Mobile-focused with offline support.
    • Works as a “SillyTavern Lite” alternative.
  • Strengths:
    • Lightweight backend with robust feature support (e.g., roleplay, text-to-speech).
    • Integrates well with Open WebUI and SillyTavern.
  • Limitations: UI is functional but lacks visual polish.
  • Link: Koboldcpp GitHub
  • Strengths:
    • Minimalist server UI with OpenAI-compatible API.
    • Excellent for developers due to fast updates.
  • Limitations: Limited feature set compared to Oobabooga and Open WebUI.
  • Link: Llama.cpp GitHub
  • Strengths:
    • Broad feature set including image generation and voice capabilities.
    • Stable for solo usage.
  • Limitations: Slower performance compared to newer backends like TabbyAPI or vLLM.
    • Open WebUI: Can integrate vision models, image generation, TTS, and more with third-party tools like Azure or Together.ai.
    • Koboldcpp: Supports some multimodal backends but lacks native syntax highlighting.
    • LM Studio: Quick model mounting for API integrations.
    • AnythingLLM: Flexible for agent development and experimentation.

  • For Beginners: LM Studio or Msty provide ease of use with minimal setup.
  • For Advanced Users: Open WebUI offers extensive customisation and backend compatibility.
  • For Roleplay: SillyTavern excels in flexibility with multiple backends.
  • For Multimodal Needs: Combine Open WebUI with specific vision or TTS backends.
  • For Developers: Use Llama.cpp for rapid updates or Koboldcpp for lightweight integration.

  • Open WebUI GitHub
  • LibreChat GitHub
  • SillyTavern GitHub
  • Koboldcpp GitHub
  • Llama.cpp GitHub
  • Oobabooga GitHub
  • Msty App
  • ChatterUI GitHub

Key Frontend Options

Open WebUI

LibreChat

SillyTavern

LM Studio

Msty

ChatterUI

Backend Integration and Performance

3.1 Koboldcpp

3.2 Llama.cpp

Oobabooga

Multimodal Support and Advanced Features

Multimodal Capabilities

Developer-Oriented Tools

Provenance