djovaneli/llmxray repository overview

⁠LLMxRay

Local LLM Observatory — See what your AI is actually doing, token by token, layer by layer.

No cloud. No API keys. No cost. Everything runs on your hardware.

⁠Quick Start

docker run -p 5174:5174 djovaneli/llmxray

Open http://localhost:5174⁠ in your browser. Make sure Ollama⁠ is running on your machine.

⁠Configuration

⁠Custom Ollama URL

docker run -p 5174:5174 -e OLLAMA_URL=http://192.168.1.50:11434 djovaneli/llmxray

⁠Custom port

docker run -p 3000:3000 -e PORT=3000 djovaneli/llmxray

⁠Docker Compose

⁠Standalone (Ollama running on your host)

services:
  llmxray:
    image: djovaneli/llmxray
    ports:
      - "5174:5174"
    environment:
      - OLLAMA_URL=http://host.docker.internal:11434

⁠With Ollama as a sibling container

services:
  ollama:
    image: ollama/ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama-data:/root/.ollama

  llmxray:
    image: djovaneli/llmxray
    ports:
      - "5174:5174"
    environment:
      - OLLAMA_URL=http://ollama:11434
    depends_on:
      - ollama

volumes:
  ollama-data:

⁠Linux Note

On Linux, host.docker.internal may not resolve by default. Either:

• Use the Docker Compose example above with Ollama as a sibling container
• Add --add-host=host.docker.internal:host-gateway to your docker run command

⁠Features

• Real-time token streaming with confidence coloring
• Model comparison (up to 4 slots side by side)
• Surgical Benchmark with real logprobs
• RAG pipeline with local document search
• Visual Tool Workshop with bidirectional code sync
• System monitor with hardware detection
• Dark and light mode

⁠Links

• Website: lognebudo.github.io/llmxray⁠
• Documentation: lognebudo.github.io/llmxray/docs/en/⁠
• GitHub: github.com/LogneBudo/llmxray⁠
• npm: npmjs.com/package/llmxray⁠

⁠License

Apache 2.0 — Copyright 2026 Ivan Stankovic (LogneBudo⁠)