Metis

🧪 Want to see the background layer in action? Try the Public Health edition.

Metis_PH is a fully worked edition that ships with a pre-loaded knowledge layer (WHO guidance + epidemiology & methods references). Clone it to test the cited, library-grounded answers immediately — without building a corpus first — then bring the same setup to your own field here.

See it in action

Why researchers trust it

• 📚 It cites your own sources. Every knowledge answer is drawn from your indexed library, with document- and page-level citations — not the model's guesses.
• 🔗 It connects everything you know. Every paper, meeting transcript, idea, note, journal entry and task is linked to the rest of your work. The grant you write today surfaces a method paper from last year and a meeting note from March — you never go looking; Metis brings it to you.
• 🧠 It routes to the right expert. Ask in plain language, and Metis hands the work to the right one of 30+ specialist skills — Librarian, Methods Coach, Writing Partner, Meeting Memory, Epidemiologist, Course Builder, and more.
• 🔁 It improves itself. After every task it logs what worked and what fell short; each week it drafts improvements to its own behaviour and waits for your approval. Most MCP servers are static — Metis gets sharper the longer you use it.
• 🚫 It refuses to invent. Ask about something that isn't in your library and Metis tells you so, instead of fabricating a plausible-sounding answer. (This grounding behaviour is covered by an automated test.)
• 🔒 It stays on your machine. Local embeddings, local database, local files. Your papers, patient-adjacent data, and unpublished work never leave your computer.

🎥 See it in action above — the dashboard, a tour of the tabs, the silent layer into Claude Desktop, and Metis improving its own work.

Easiest way to try it: install Claude Desktop and run the 3-step setup — a demo workspace is pre-loaded, so you start with something to explore instead of a blank screen.

Who is this for?

🔬 I'm a researcher

No programming background needed. Install in minutes, start working immediately. Everything Metis does is explained in plain language.

→ Get started (3 steps)

⚙️ I'm a developer

Open-source, extensible, well-architected. Build domain packs, add agents, extend the MCP server, or deploy for your institution.

→ Explore the architecture

What is Metis?

Metis is a research companion built on top of Claude that keeps your data on your own machine. It gives every AI conversation a persistent memory of your domain, your papers, your projects, and your working history. It routes your requests to the right specialist, does the work, records the result, and returns a plain answer — without requiring you to prompt or configure anything.

The app runs on your machine and your data stays there — your documents, notes, embeddings and memory never leave it. The reasoning is powered by Claude, so the text you choose to send for analysis goes to the Anthropic API; everything else is local. (See Data Protection for exactly what leaves your machine, and when.)

The short version: imagine an AI that already knew your field and your literature, connected every paper, meeting, idea and note you've captured, sent each request to the right specialist — and got sharper about your work, and about itself, the longer you used it. That's Metis.

How it works

Metis is not a separate app you log into. It's a small service that runs quietly in the background and connects Claude to your research — your papers, your memory, your projects.

1. A background service (the "MCP server") starts with your computer. It's the bridge between Claude and your files — you never interact with it directly.
2. You talk to Metis through Claude, two ways:
   • Claude Desktop (easiest): open it and pick a Metis prompt (e.g. Metis, Metis Doctor) from the prompt menu — or just ask.
   • Claude Code (terminal): type /metis followed by your request.
3. You ask in plain language. Metis works out which of its 30+ specialists should handle it, does the work using your library and memory, and answers — citing sources.

That's it. There's nothing to learn before you start; the dashboard is optional visibility on top of all this.

Design Philosophy

Every AI conversation starts from zero. You spend ten minutes re-explaining your context, and when the session ends, it's gone. Generic AI tools are powerful but stateless — they know everything about the world and nothing about you.

Metis is built on one idea: the AI should know you. And it should keep getting better — on its own.

Not just your name — your domain, your literature, your projects, your preferred working style, your open questions, your meeting notes from last month, and the paper you added to your library yesterday. The longer you use Metis, the better every response gets. Not because the AI changes — because Metis knows you better.

You don't need to follow developments in AI. Metis does that for you. Every week, Metis reviews its own performance across all your sessions, identifies where it could have done better, drafts improvements to its own behaviour, and waits for your approval before applying them. As better methods and models become available, those improvements are folded in the same way — always proposed for your approval, never applied behind your back. As a researcher, you focus on your research. Metis handles keeping itself sharp.

The core mechanism is cross-pollination. Every time you capture an idea, add a paper, record a meeting, or complete a task, Metis connects it to everything else in your research universe. A paper you indexed a year ago surfaces when you're writing a grant today. A meeting note from March links to the idea you captured this morning. An open question from six months ago connects to a new paper that just came out. These connections happen automatically, in the background, without you having to search for them. This is what makes Metis a research companion rather than a search tool — it thinks across your entire body of work so you

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