Quick Start

One command. 30 seconds.

npx llmxray

Or with Docker:

docker run -p 5174:5174 djovaneli/llmxray

Open http://localhost:5174 and start chatting. That's it.

Prerequisite: Ollama running locally with at least one model pulled (ollama pull llama3.2).

Why LLMxRay?

You run a local LLM. You chat with it. But what actually happened?

• How fast was each token? Which ones was the model confident about?
• Is the response quality degrading over long conversations?
• What would this have cost if you ran it in the cloud?
• Is the model repeating itself? Refusing? Generating gibberish?
• How does temperature 0.3 compare to 0.9 on the same prompt?

LLMxRay answers all of these, visually, in real time, for free.

Features

Real-Time Chat with Token Intelligence

Chat with any Ollama model and watch tokens arrive with confidence coloring — each token is tinted based on generation speed. Supports markdown, multi-turn conversations, file attachments, vision models, and slash commands.

Response Quality Gates

Every response is automatically analyzed. Colored badges appear only when something is wrong:

• Repetition — excessive repeated phrases (4-gram analysis)
• Refusal — "as an AI language model" and 7 other patterns
• Gibberish — high non-ASCII ratio
• Empty — fewer than 10 words
• Truncation — hit the token limit without finishing

Model Comparison Workbench

Up to 4 slots with independent model, temperature, and system prompt. Features include side-by-side streaming, word-level diff highlighting, metrics comparison, and one-click presets (Temperature Sweep, Deterministic Pair, Language Compare with Token Tax visualization).

Performance Analytics

• Latency percentiles (P50/P95/P99) for duration and TTFT
• Error intelligence — 7-category classifier with timeline
• Usage heatmap — 7x24 grid of your active hours
• Settings impact — temperature vs tokens/sec scatter plots
• Cold vs warm start tracking with model load history

Cost Dashboard

Token usage per model/day with estimated cloud-equivalent pricing. See what you're saving by running locally.

Surgical Benchmark

Test model knowledge with multi-choice question suites. Uses real logprobs via OpenAI-compatible endpoint for accurate confidence measurement. Build custom suites visually or let AI generate them from a topic.

Embeddings Lab & RAG Pipeline

Embed text, visualize vectors, measure cosine similarity. Build a local knowledge base from PDFs, DOCX, and CSV — chunked, embedded, and searchable. All stored in IndexedDB. Zero cost.

Tool Workshop (Visual Canvas)

Drag-and-drop node canvas for building tool definitions. Bidirectional code sync (edit nodes or TypeScript — both update). Probe APIs, auto-generate schemas, test with live execution.

AI Training Pipeline

Curate training data from your conversations. Tag, review, and export as JSONL for fine-tuning.

Local AI History Database

Every experiment (benchmarks, comparisons, chats, training pairs) is automatically archived in a queryable IndexedDB database with filters, trends, exports, and retention policies.

Multilingual

Full translations in English, French, Chinese, and Arabic. RTL layout support. Community scaffolds for Hebrew and Japanese.

Screenshots

Chat with token streaming and confidence

Model comparison — side by side

Session deep dive — metrics and timing

Benchmark with confidence radar

Embeddings — cosine similarity

System monitor — hardware and Ollama status

Who Is This For

| You are... | LLMxRay helps you... | |---|---| | Developer | Debug prompts, profile latency, compare models, inspect tool calls, track costs | | Researcher | Run controlled experiments with consistent settings across models and temperatures | | Student / Educator | Explore model behavior visually — built-in Educators Kit with 9 interactive modules | | AI team lead | Understand quality trends, error patterns, and resource usage across your local fleet |

Install Options

npx (recommended)

npx llmxray
npx llmxray --port 3000
npx llmxray --ollama-url http://192.168.1.50:11434

Docker

docker run -p 5174:5174 djovaneli/llmxray
docker run -p 5174:5174 -e OLLAMA_URL=http://host.docker.internal:11434 djovaneli/llmxray

From source

git clone https://github.com/LogneBudo/llmxray.git
cd llmxray
npm install
npm run dev     # http://localhost:5173

Tech Stack

| Layer | Technology | |---|---| | Framework | Vue 3.5 + Composition API | | Language | TypeScript 5.9 (strict) | | Build | Vite 7.3 | | Styling | Tailwind CSS 4.2 | | State | Pinia 3 (store-per-concern) | | Charts | Chart.js 4, D3.js 7 | | Canvas | Vue Flow (visual node editor) | | Code Editor | CodeMirror 6 | | Storage | IndexedDB (browser-native) | | LLM Backend | Ollama (local) |

Architecture

Streaming — Reads Ollama NDJSON via fetch() + ReadableStream. Tokens update the UI reactively through Pinia stores.

Token confidence — Approximated from inter-token latency (faster = more confident). Clearly labeled as approximation. Benchmarks use real logprobs via OpenAI-compatible endpoint.

Store-per-concern — Each domain has its own Pinia store: tokens, sessions, metrics, reasoning, comparison, embeddings, quality, cost, and more.

Hardware detection — Custom Vite plugin queries the OS directly (PowerShell/proc/sysctl) for accurate hardware specs.

Development

| Command | What it does | |---|---| | npm run dev | Dev server (port 5173) | | npm run build | Type-check + production build | | npm run test | Unit tests (Vitest) | | npm run test:e2e | End-to-end (Playwright) |

Contributing

Contributions welcome! See CONTRIBUTING.md for setup and guidelines.

Community translations especially welcome — scaffold files ready for Hebrew and Japanese.

License

Apache License 2.0

Trademark

LLMxRay is a trademark of Ivan Stankovic (LogneBudo). See TRADEMARK.md.