Chrome DevTools for AI models and agents — see attention, features, and agent steps inside any local model.
Project description
ModelMRI
Chrome DevTools for AI models and agents. Load any local model — LLM, VLM, or robot policy — and see inside it while it runs: attention, features, circuits, agent steps. Local-first. MIT.
🚧 Under active construction, in public. v0.1 (live attention playground) ships in ~2 weeks. Follow the build: Substack
Why
When a model gives a wrong answer, you can't see why. When an agent fails at step 47, you get a wall of logs. When a robot policy drops the object, you get nothing. The research tools that can see inside (SAEs, attention analysis, circuit tracing) live in notebooks only specialists can drive.
ModelMRI packages that research into a tool with the ergonomics of browser DevTools: one-line install, open localhost:5900, look inside.
Planned (the 12-week public roadmap)
- v0.1 — load a HuggingFace LLM, watch attention flow live at 60fps (WebGL)
- v0.2 — SAE feature browser: see the concepts inside the model, steer them
- v0.3 — agent mode: record any Anthropic-SDK/Claude-Code agent run, replay it, find the failing step
- v0.4 — the first interactive tool for looking inside a robot policy (SmolVLA + LeRobot)
- v0.5 — polish, zero-install hosted demo, launch
Install
pip install modelmri
modelmri serve # then open http://localhost:5900
Or from source (dev):
git clone https://github.com/muhammadmahadazher/ModelMRI && cd ModelMRI
cd frontend && npm install && npm run build && cd ..
uv sync
uv run modelmri serve
Click Load Qwen2.5-0.5B-Instruct (~1 GB one-time download), type a prompt, watch tokens stream — then hover any token to see what the model attended to.
Status
| Piece | State |
|---|---|
| FastAPI backend: model loading, REST, WebSocket token streaming | ✅ working |
| Built-in playground page (temporary, pre-React) | ✅ working |
| Attention inspector: hover any token, see what it attended to | ✅ working |
| SAE feature browser | 🏗️ next |
| Frontend (React + WebGL) | 🏗️ next |
pip install modelmri |
placeholder published (real release at v0.1) |
MIT © Muhammad Mahad Azher
Release history Release notifications | RSS feed
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