LogneBudo/llmxray

Launch
See what your local AI is actually doing.
Point VS Code Copilot, Open WebUI, Continue, n8n or your own agent at LLMxRay instead of Ollama, and see every turn:
what was cached, what was read again, what the daemon silently cut, which tool evicted which model. No cloud. No API keys. No cost.
🌐 English •
Français •
中文 •
العربية •
Srpski
Quick Start •
What your tools send •
Send it on •
For agents •
Also in the box •
Who is this for •
Install •
Ollama compatibility •
Changelog
## Quick Start
You need Ollama running with at least one model pulled (ollama pull llama3.2). Then:
bash
npx llmxray
Or with Docker:
bash
docker run -p 5174:5174 -p 11435:11435 djovaneli/llmxray
Now point your tools at http://localhost:11435 instead of Ollama's 11434: VS Code Copilot, Open WebUI, Continue, n8n, your own scripts. It behaves exactly like Ollama and forwards everything. Open http://localhost:5174, go to Live, and watch their turns arrive.
Just want to try a model? Open the Chat page, pick one, and send a message.
## What your tools really send
Your editor, your agents and your scripts all talk to the same Ollama. These pages show what each of them sends, what the daemon does with it, and what that costs the others.
### Live: what your other tools send
LLMxRay opens a second port, 11435, that behaves exactly like Ollama and forwards everything to it. Point VS Code Copilot, Open WebUI, Continue, n8n or your own script at http://localhost:11435 and every turn they send appears here: client, model, prompt size, how much came from the cache, prefill and generation speed, tool calls.
VS Code Copilot ─┐
Open WebUI ──────┤ ┌─ Live page: every turn,
Continue / n8n ──┼─► http://localhost:11435 ─► Ollama ┤ cache hits, prefill speed,
your scripts ────┘ (LLMxRay) └─ where the cache broke
Select a turn to see what the prompt is made of and the exact point where it stopped matching the previous one, which is where the cache broke. Findings are plain sentences, each linked to the turn that proves it: a client re-reading its whole prompt every turn, a prompt the daemon silently cut to fit the context (a chat that outgrew it loses its oldest messages; instructions too long on their own lose their start; Ollama says nothing either way), a model running partly on CPU, tool schemas taking 40% of every prompt. Only counts and timings are kept, on this machine for 30 days, so a restart picks up where it stopped. Prompt text is an opt-in that writes to a local file. Nothing leaves your machine unless you send the turns on to your own trace viewer: each model call as an OpenTelemetry span, each run as a trace, for Jaeger, Phoenix, Langfuse or Grafana.
Both ports answer only this machine by default, because the Live log holds whatever your tools sent. Start with npx llmxray --host 0.0.0.0 to let other machines on your network reach them, for a Live page on a second screen or a shared GPU box.
### Prompt X-ray: the prompt itself, drawn to scale
Each turn your tool sent, as a bar. Colour is what each part of the prompt was: system prompt, tool schemas, earlier turns, the new message. Brightness is what happened to it: dim was already in the model's memory and cost nothing, bright had to be read again, striped was cut by the daemon and never seen. Stack the turns of a conversation and you watch the cache grow, see the turn where a timestamp in the system prompt emptied it, and the turn where the conversation outgrew the context and Ollama silently dropped the oldest messages and answered anyway. When the system prompt alone is too long, it is its start that goes. Click a part and the request opens at that place in the text.