Metis — Research Cortex MCP Server

A private, local research "second brain" for Claude — cited answers from your own library (it won't invent what it can't find), persistent project memory, daily briefs, a live meeting assistant, and 34 specialist agents. Runs entirely on your machine.

GitHub

Documentation

Metis — Research Cortex

Metis — The Research Cortex

AI built around researchers. Not a prompt box — a way of working.
Your papers, meetings, ideas, notes and journal — each one linked to the rest.
A research companion that reviews its own work and gets sharper every week.

It's 7:20. You open the dashboard. The morning brief reads:

"Three papers matching your configured topics landed overnight — one directly challenges a working hypothesis in your field. Your literature coverage in methods has grown to 84%. I've cross-referenced all three with your knowledge graph, connected them to your meeting note from Tuesday, and flagged four passages for your review. One tracked analysis is approaching a key deadline."

No prompt. No setup. Your research, connected — every morning.

☁️ Light
MCP server only

You ask Claude to build a monitoring dashboard for your Leishmaniasis project. Metis recalls the surveillance tool you built eighteen months ago, your preferred layout, and your standard epidemiological indicators. The Epidemiology Agent and Dashboard Maker deliver exactly what you need — in your style, to your domain standards — without any re-explaining.

🔗 Cross-pollination
The moment everything connects

You capture a quick idea about a novel surveillance approach. Within seconds, Metis surfaces three things you'd forgotten existed: a methodology paper from fourteen months ago that used a similar approach, a meeting note from March where your field partner described the same barrier, and an open question you logged after a conference. You hadn't connected any of it. Metis did. The grant section writes itself.

🌐 Metis OS
The full picture — in development

Your calendar shows a meeting with a Lepra diagnostics colleague. Yesterday you captured an idea about rapid field tests. Metis has your April transcript with this person and this week's new papers. A briefing appears before you leave. After the meeting, you ask for a five-day course on Lepra diagnostics from the latest research. By evening, it's ready.

Editions: Metis — Base shell · Metis_PH — Public Health & Epidemiology · Metis_BM — Biomedical Sciences (coming soon) · Metis_CL — Clinical Sciences (coming soon)

Last commit

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 two minutes — (demo coming) watch Metis answer from a real WHO source with page citations, then refuse to summarise a protocol that doesn't exist.

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.

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.
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.
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 don't have to hold it all in your head.

This is genuinely new ground. The individual components — local language models, retrieval-augmented generation, agent routing, vector search — all exist independently. What Metis presents is a coherent integration of all of them, purpose-built for the specific demands of research work: long timelines, sensitive data, deep literature, and knowledge that accumulates over years. A system that grows with you, and surfaces connections for you — rather than starting from zero every session. To our knowledge, nothing quite like this exists as a unified, locally-running, researcher-facing system.

Three levels — choose your entry point

Level	What it is	Best for
☁️ MCP server only	A background service that runs alongside Claude. Persistent memory, session awareness, 30+ specialist agents — no dashboard, no visible app.	Researchers who use Claude already and want it to know their work
📊 With the dashboard	Full visibility across your research life — papers, ideas, meetings, tasks, projects, all connected. Built for cross-pollination (ideas linking to literature) and brain off-loading (tracking leaving your head, entering the system).	Researchers who want a complete research operating environment
🌐 Metis OS	Connects to email, calendar, data systems, and institutional infrastructure — a unified intelligence layer for your entire working environment.	The longer vision. Still in development.

Where things stand today: The MCP server, 30+ agents, and the 9-tab dashboard are fully operational and used daily. The one-click installer and the pre-loaded domain knowledge layer are still being refined. This is a working system — not vaporware — but it is also not finished. If something breaks, please open an issue. That feedback shapes what gets built next.

For Researchers

No programming background needed. Everything below is point-and-click or copy-paste.

Install in 3 steps

Step 1 — Get your Anthropic API key (free, 2 minutes)

Go to console.anthropic.com and create an account.

Click API Keys → Create Key. Copy the key (it starts with sk-ant-…).

Keep that tab open — the installer will ask for it once.

The key stays on your computer. It is never uploaded or shared.

Windows

⬇ Download MetisSetup.exe

Double-click the installer. The wizard walks you through:

Full or AI only — Full gives you the AI assistant + 9-tab research dashboard (~15 min). AI only is faster (~5 min) and you can add the dashboard later.
Your projects — Tell Metis what you're working on. It creates a tracking record for each project, writes a context file into the project folder, and registers it in Claude Desktop automatically.
Demo workspace — Pre-loads realistic example projects, meetings, literature, and tasks so you can explore every feature immediately. Recommended for first-time users.
API key — Paste it once.

Everything else is automatic. Claude Desktop opens at the end with Metis ready to go.

Requirements: Windows 10 or 11 · Internet connection · API key

macOS or Linux

Open Terminal and paste:

bash <(curl -fsSL https://raw.githubusercontent.com/SVerITG/Metis_PH/main/system/mcp-server/setup-mcp.sh)

The script asks two questions (Full or AI only, demo workspace) and does the rest. Registers Metis with Claude Desktop and Claude Code automatically. Works on Ubuntu 20/22/24, Debian, and macOS.

Requirements: Python 3.10–3.13. The installer prefers uv (which downloads its own Python 3.12 — no system packages needed). If uv isn't available it falls back to your system Python; on a bare system you may need sudo apt install python3-venv. Very new Python (3.14+) isn't supported yet — some packages don't publish wheels for it. If you hit "ensurepip is not available", install uv (the line above) or python3-venv and re-run.

What you get on day one

Feature	What it does
30+ specialist agents	Librarian, Epidemiologist, Methods Coach, Writing Partner, Meeting Memory, Course Builder, Career Coach, Critic, and more — each an expert in their domain
Grounded answers	Every knowledge question is automatically answered from your own indexed document library with page-level citations — not AI guesses
Library management	Import PDFs, sync Zotero or Mendeley, ask "what do my papers say about X?" — cited answers from your own library
Morning intelligence brief	Every morning: new papers on your exact research topics, field news, surveillance alerts, and a focus recommendation — fully personalised
Live meeting assistant	Follow along in real time, or paste a transcript — get structured notes, action items, and project cross-references automatically
Project tracking	Every project gets a tracking record, a context file in its folder, and integration with Claude Desktop. The Update button scans all your project folders for activity.
Voice capture	Record anywhere, transcribe locally (no API, no upload), route to ideas, journal, or notes
9-tab dashboard	Today · Knowledge · Meetings · Learning · Work · Thinking · Planner · Teach · Metis — all live, all local
Data protection	Six security layers + the `/safe-analysis` workflow. Sensitive data is detected and held back before it reaches the AI, and the recommended pattern keeps raw data on your machine entirely — you share only derived metadata.
Cross-pollination	Every idea, paper, meeting, and task is automatically connected to everything else in your research universe. Metis surfaces links across time — a paper from last year, a meeting note from March, a question you logged at a conference — without you searching for any of it.
Token tracking	Every agent run shows exactly what it cost — which specialist was used, how many tokens, what model. The dashboard Today tab has a live token pulse so you always know your daily usage. Most daily tasks stay under a few cents.
Tool subset loading	Metis registers 170+ MCP tools, but exposing all of them to Claude on every session wastes context. By default, Metis loads only the tools relevant to the current agent — 90 tools for News Radar, 107 for the Librarian, ~65 for a general session. Each tool definition costs tokens; loading fewer means more room for actual work and lower per-session cost. Disable with `METIS_TOOL_SUBSETS=0` to see all tools.
Metis evolves — you don't have to	Every week, Metis reviews its own session logs, identifies where it underperformed, and drafts behaviour improvements. You approve or reject them — nothing changes without your sign-off. New capabilities are folded in the same way. You focus on your research; Metis keeps itself sharp.
Grows with you	Every agent run adds to your profile. A question asked after six months of use gets a meaningfully better answer than the same question on day one — not because the AI changed, but because Metis knows you better.

Key Workflows

Morning

Wake up
  └─ Metis scanned overnight
       ├─ New papers on your configured research topics
       ├─ Surveillance alerts and field news
       ├─ Tasks due today, overdue items
       └─ Suggested daily focus based on your open projects
           └─ Open dashboard → read morning brief → start work

Literature

New paper (PDF / DOI / Zotero / Mendeley import)
  └─ Librarian indexes it
       ├─ Added to knowledge graph
       ├─ Cross-pollinated with existing papers, past ideas, meeting notes
       └─ Available for cited semantic search immediately
           └─ Ask: "What do my papers say about X?"
                └─ Answered with inline citations from your own library

Meetings

Meeting ends
  ├─ Paste transcript (Teams / Zoom / any audio file)
  └─ Meeting Memory agent processes it
       ├─ Structured notes with context
       ├─ Action items: who does what, by when
       ├─ Cross-references to your projects and open questions
       └─ Follow-up tasks auto-added to Work tab

Ideas and writing

Idea surfaces
  └─ Ctrl+K → capture instantly (i: idea · n: note · t: task · q: question)
       └─ Metis cross-pollinates immediately
            └─ Related papers + past ideas surfaced automatically
                 └─ Writing Partner → draft · Librarian → sources · Methods Coach → check argument

Teaching and courses

Course topic defined
  └─ Course Builder
       ├─ Generates lessons, slides, assessments, question banks
       ├─ Flags new papers relevant to your course automatically
       ├─ Gap analysis against current literature
       └─ Spaced repetition for your own knowledge maintenance

The Dashboard

The 9-tab dashboard runs locally at http://127.0.0.1:8080. No account, no cloud, no subscription.

Metis dashboard — Today tab

The Today tab — morning briefing, active project, progress, news radar, and quick stats. Everything personalised to your research domain.

Tab	What it does
Today	Morning brief, priority task queue, news rail, quick capture (`Ctrl+K`)
Knowledge	Semantic PDF search, literature cards, knowledge graph, coverage gap analysis
Meetings	Live assistant, transcript import, action items, cross-references
Learning	Course progress, spaced repetition, competency map
Work	Tasks, project cards, activity tracking, one-click open in VS Code / RStudio / Claude
Thinking	Idea capture, cross-pollination, brainstorm launcher, open questions tracker
Planner	Kanban board, research timeline, milestones
Teach	Course Builder, literature alerts, lesson generation, student-facing content
Metis	Agent run history, self-improvement proposals, system health, identity card

How Metis Knows You

When you first install Metis, a setup wizard walks you through your profile:

research domain · specific interests · active projects · working style · tools you use · data sensitivity level

This creates your identity card — a living profile that every agent reads before responding to you. It grows over time. Every session adds context. Every idea you capture tells Metis what you're thinking about.

A question asked after six months of use gets a meaningfully better answer than the same question on day one — not because the AI changed, but because Metis knows you better.

Data Protection

Researchers handle sensitive data. Most AI tools don't take that seriously.

Patient data, embargoed results, unpublished findings — these should never leave your machine. Metis was designed with this in mind from the start.

What leaves your machine (and when):

Service	What	When	Optional?
Anthropic Claude API	Text you send for analysis	On demand	Required for AI
PubMed / OpenAlex	Your research search keywords	Daily morning scan	Yes
Zotero	Library metadata (titles, abstracts, tags)	Daily sync	Yes
CrossRef	DOI queries	On demand	Yes
HuggingFace	Model name only — downloads embedding models	First run	Yes

Everything else — your documents, voice recordings, PDF text, meeting notes, patient-adjacent data — stays on disk.

Security layers:

Layer	What it does
Pre-tool hook	Checks every tool call for injection attempts and restricted paths; peeks at a data file's header locally before it's read and asks you to confirm before individual-level data is loaded into the conversation
PII detection	11 checks, 4-level classification. Sensitive data is classified and refused at pipeline entry
Injection probe	Detects prompt injection in external content (papers, transcripts)
Constitution	14 machine-readable rules applied to every deep agent run
Red lines	5 non-overridable rules enforced at code level — no override possible
AES-256 encryption	All backups encrypted at rest

The recommended pattern for sensitive data: send code, not data.

The strongest protection isn't a scanner — it's never putting the raw data in a prompt at all. Metis is built for this. Ask it for an analysis script (R or Python); you run it on your own machine against your real data; and only the derived outputs — variable names, value counts, summary tables, model coefficients, a data dictionary — come back to Metis. Claude reasons over the shape of your data, never the records.

Your real dataset (patient rows)         ── stays on your machine, never sent ──┐
        │ you run Metis's R/Python script locally                               │
        ▼                                                                       │
Derived metadata (column names, unique values, Table 1, model summary) ── safe to share ──► Metis
        │                                                                       │
        ▼                                                                       │
Metis builds the dashboard / writes the methods / interprets the model ◄────────┘

This is exactly how the dashboards and analyses in our own work were built: the raw surveillance data never left the machine, yet Metis could profile it, name every variable, list unique values, and generate a full dashboard.

Just run /safe-analysis (Claude Code or Claude Desktop) and Metis walks you through it end-to-end — it proposes the local script, tells you exactly which metadata to paste back, and never asks for raw rows. Two backstops sit underneath: the pre-tool hook peeks at a data file's header locally and asks before any individual-level data is read into the conversation, and the Data Guardian (PII scan + 4-level classification at pipeline entry) catches sensitive content that slips into a prompt anyway. With the pattern above, neither usually has to fire.

How Metis Stays Current — So You Don't Have To

AI is moving fast. New models, new capabilities, new research tools appear every month. Most researchers don't have time to follow it. Metis is designed to handle this for you.

After every agent run, Metis logs a reflexion — what went well, what fell short, what context was missing. Every week it aggregates these into themes. Every week it drafts behaviour improvements with a clear rationale. You review the proposals in the Metis tab — one click to approve, reject, or edit — and the approved changes are written to disk.

This means Metis gets better at working with you specifically, week after week. It also means that as new AI developments become available and get integrated into Metis, you receive the improvements without having to do anything. Your job is your research. Metis's job is to stay sharp.

The self-improvement loop in detail:

After every agent run — reflexion logged: what went well, what could improve, what was missing
Weekly — themes extracted across all sessions; patterns identified
Proposal drafted — a concrete proposed change to agent behaviour, with reasoning
You review in the Metis tab — approve, reject, or edit before anything applies
Applied with backup — the update is written with a timestamped rollback point

No change to Metis's behaviour ever happens without your explicit approval. The system proposes; you decide.

For Developers

This section assumes familiarity with Python, Git, and the command line.

Architecture

flowchart LR
    U([Researcher])
    subgraph Harness["AI Harness (Claude Code / Desktop)"]
        METIS[Metis\nrouter agent]
        AGENTS[Specialist agents\n30+ agents]
        WATCHERS{{Watchers\nData Guardian · Cybersecurity}}
    end
    subgraph Platform
        MCP[MCP Server\n170+ tools\nFastMCP]
        DASH[Dashboard\nFastAPI + HTMX]
        DB[(SQLite\nWAL mode)]
    end
    subgraph Memory
        EPIS[Episodic]
        SEM[Semantic\nvector search]
        REFLEX[Reflexion log]
    end
    Skills[/CLI Skills\n/metis · /librarian · …/]

    U -->|asks| METIS
    U -->|clicks| DASH
    METIS -->|routes to| AGENTS
    AGENTS -->|uses| MCP
    MCP --- DB
    DASH --- DB
    WATCHERS -.guards.-> AGENTS
    AGENTS -->|writes| REFLEX
    REFLEX -->|proposes edits to| AGENTS
    MCP --- Memory
    Skills --> METIS

    style WATCHERS fill:#fff4e6,stroke:#9a7b3c
    style REFLEX fill:#eef4f1,stroke:#2d4a3a,stroke-dasharray:3 3

Stack

Layer	Technology
AI harness	Claude Code, Claude Desktop (primary); Gemini (experimental)
MCP server	Python 3.10+, FastMCP, runs in local venv
Dashboard	FastAPI + HTMX + Jinja2 — no JavaScript framework
Database	SQLite WAL mode, 46 tables
Vector memory	sqlite-vec + nomic-embed-text-v1.5-Q (768 dims, local ONNX)
Semantic PDF search	sqlite-vec — local PDF chunk index, no external API
Host OS	Windows + WSL2 (Ubuntu 20/22/24) · macOS · Linux

Memory — 5 layers

Layer	What it stores
Episodic	Session events and observations (discovery · decision · implementation · issue)
Semantic	Vector-indexed content (sqlite-vec + nomic-embed-text-v1.5-Q, 768 dims)
Procedural	Skill files and agent contracts — the agent's persistent behaviour
Working	Active session context and current project focus
Reflexive	Reflexion log and improvement proposals

Knowledge Layer & Grounded Answers (RAG)

When you ask a knowledge-intensive question, Metis retrieves relevant passages from your indexed document library before the specialist agent answers. The agent grounds its response in what it can read from your library — not only what it was trained to recall.

You ask Methods Coach:
"Which variance estimator should I use for my Poisson MLM with overdispersion?"

Metis retrieves before routing:
  → Leyland (2020) Multilevel Modelling for Public Health, p.142 — score 0.87
  → Bates lme4 vignette, p.28 — score 0.71

Methods Coach answers grounded in those passages, citing both sources.

Component	Details
Embedding model	`nomic-embed-text-v1.5-Q` — 768-dim, ONNX, fully local
Vector store	`sqlite-vec` virtual table inside Metis SQLite database
Chunking	3,200-character chunks, 400-character overlap
Score threshold	Chunks below 0.4 similarity dropped before injection

Pre-loaded knowledge layers (Metis_PH):

Layer	Documents	Covers
Public Health Background	34	WHO guidelines, global health reports, social determinants, NCDs, maternal & child health
Epidemiology & Methods	10	STROBE, WHO Basic Epi, Leyland MLM, Bates lme4, PRISMA 2020, SaTScan, CIFOR

Security Layers (detail)

pre-tool-use.mjs — 13 injection patterns, domain allowlist, path restrictions (every tool call)
guardrails.py — injection probe on all external content (papers, web, transcripts)
safety.py — 11 PII checks, 4-level classification, sensitive data refused at pipeline entry
constitution.md — 14 machine-readable rules for deep and chained agent runs
red-lines.md — 5 non-overridable rules enforced at code level

Token Efficiency

Model routing — Haiku for triage/summaries, Sonnet for most work, Opus only for deep reasoning; most daily usage never touches Opus
Surgical context assembly — each agent gets only the context relevant to its task, not full history
Max-turns guardrail — stops at 20 turns, prompts /clear
Session handoff — under 3 KB state capture at session end; no re-paying for context already established
Token pulse widget — real-time usage visible in the dashboard

Cross-AI Support

Harness	Status
Claude Code	✅ Primary — full MCP + skills + hooks
Claude Desktop	✅ Primary — full MCP + memory; no CLI skills
Gemini 2.0+	🔬 Experimental
OpenAI / Cursor	🟡 Partial — MCP tools only

Installation Options

Option 1 — Single command (Linux, macOS, WSL)

bash <(curl -fsSL https://raw.githubusercontent.com/SVerITG/Metis_PH/main/system/mcp-server/setup-mcp.sh)

Detects Ubuntu 20/22/24, Debian, macOS Homebrew. Creates venv, installs all dependencies, registers with Claude Code and Claude Desktop. Idempotent — safe to re-run.

# Profile overrides (skip the interactive menu):
METIS_PROFILE=light    bash <(curl -fsSL ...)   # MCP server only (~5 min)
METIS_PROFILE=standard bash <(curl -fsSL ...)   # MCP + dashboard (~15 min)
METIS_PROFILE=full     bash <(curl -fsSL ...)   # Standard + scheduler (~25 min)

Option 2 — Clone and install (any platform)

git clone https://github.com/SVerITG/Metis_PH.git
cd Metis_PH/system/mcp-server
bash setup-mcp.sh

Option 3 — Manual

git clone https://github.com/SVerITG/Metis_PH.git
cd Metis_PH/system/mcp-server
python3 -m venv .venv && source .venv/bin/activate
pip install -e "."

export METIS_RC_ROOT="$(pwd)/../.."
export ANTHROPIC_API_KEY="sk-ant-..."

python -m metis_mcp.server          # MCP server
cd ../app-py && bash run.sh         # Dashboard → http://127.0.0.1:8080

Option 4 — Docker (platform test matrix)

# Full platform test — Ubuntu 24/22 + Debian in parallel
docker compose -f system/install/docker/docker-compose.test.yml up --build

# Production stack
docker compose -f system/install/docker/docker-compose.yml up -d

Register with Claude Code

~/.claude/settings.json:

{
  "mcpServers": {
    "metis-rc": {
      "command": "/home/<username>/.local/share/metis-mcp/run.sh"
    }
  }
}

Register with Claude Desktop (Windows + WSL)

%APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "metis-rc": {
      "command": "wsl.exe",
      "args": ["-e", "bash", "/home/<username>/.local/share/metis-mcp/run.sh"]
    }
  }
}

Configuration

File	Controls
`system/config/user-config.yaml`	Domain, interests, style — generated by setup wizard
`system/config/constitution.md`	14 rules applied to every deep/chain run
`system/config/red-lines.md`	5 non-overridable rules
`system/config/token-guardrails.md`	Model routing, handoff thresholds
`agents/<name>/skill.md`	Behavioural contract per agent — directly editable
`.claude/hooks/pre-tool-use.mjs`	Security gate on all tool calls

Dependencies

Package	Purpose
`mcp`, `fastmcp`	MCP protocol
`fastapi`, `uvicorn`, `starlette`	Dashboard
`sqlite-vec`	Local vector search
`onnxruntime`, `tokenizers`	Local embeddings (no API)
`feedparser`	RSS feed parsing
`pyyaml`	User config
`httpx`	Async HTTP
`pandas`, `openpyxl`, `pyreadstat`	Data analyst tools
`cryptography`	AES-256-GCM backup encryption
`pyzotero`	Zotero sync
`bibtexparser`	Mendeley BibTeX import
`anthropic`	Claude API

Editions and Roadmap

Metis ships in distinct editions — a domain-agnostic base shell, and domain packs that add field-specific content on top.

Repository	Status	What it is
Metis	✅ Live (v1.0)	Domain-agnostic base shell. Full architecture, no domain content. Clone this to build your own edition.
Metis_PH	✅ Live (v1.0, this repo)	Public Health & Epidemiology — MCP server, 30+ agents, dashboard, knowledge layer
Metis_BM	🧬 Planned	Biomedical Sciences
Metis_CL	🏥 Planned	Clinical Sciences
Metis [Community]	🌍 Open	Domain packs for other research fields — contributions welcome
Metis Institute Edition	🏛 Future	Multi-user, shared knowledge base, institutional deployment

What's in each domain edition: pre-configured journals + RSS feeds · specialist agents · domain ontology · curated background knowledge library

Want to build a domain pack? Fork Metis, add your field's knowledge library, agents, and RSS feeds, and open a PR.

Course Packages (Coming Soon)

Standalone course packages you can drop into any Metis installation:

Package	What it covers
Sampling Strategies	Probability and non-probability sampling, sample size, complex survey designs, weighted estimation
Spatial Epidemiology	Spatial autocorrelation, kernel density, SaTScan, LISA, disease mapping in R and GeoDa
Genomic Surveillance	Pathogen sequencing in public health, phylogenetics, WGS pipelines, Nextstrain

Open an issue with label course-package to pilot or contribute.

Development Status

Area	Status
MCP server (170+ tools)	✅ Operational, used daily
30+ specialist agents	✅ Operational, used daily
9-tab dashboard	✅ Operational, some features in active development
Windows .exe installer	🔧 In refinement
Docker images	✅ Test matrix working
Domain knowledge layer (Metis_PH)	🔧 Actively being expanded
Automated daily tasks (APScheduler)	📋 Next
Test suite	📋 Next
Telegram capture bot	📋 Planned
Metis OS (calendar, email integration)	🌐 Future vision

Contributing

Metis is designed to grow beyond one domain and one researcher. Contributions are welcome — especially from researchers who use it and know what's missing.

See CONTRIBUTING.md for detailed guidelines.

Most Wanted

Domain packs — the most impactful contribution. A domain pack adds: key journals + RSS feeds · specialist agents · a domain ontology · a curated background library

Domain	Status
Public Health & Epidemiology	✅ Included
Social Sciences	🔬 Planned
Biomedical / Clinical Research	🔬 Planned
Environmental Science	🔬 Planned
Economics and Development	🔬 Planned
Psychology and Behavioural Sciences	🔬 Planned
Education Research	🔬 Planned
Nursing and Allied Health	🔬 Planned

Other high-impact contributions:

Translations — the wizard and skill files are English-only; translations into French, Dutch, Spanish, German would open Metis to many more researchers
Installer testing — Windows .exe and PowerShell on managed machines, corporate environments, and varied hardware; reports of what works and what breaks are valuable
New agents and skills — specialist agents for use cases not yet covered
Security verification — independent review of the data-stays-local guarantees (what's kept on the machine vs. sent to the Claude API), PII detection, hook behaviour, and constitution enforcement; if you find a gap, open a private issue
Multi-AI support — better Gemini and local model (Ollama) support, especially for offline research environments
Bug reports and UX feedback — if something doesn't work for your workflow, say so

Changelog

Post-v1.0 — June 2026

What changed
Sensitive-data workflow (`/safe-analysis`) — a first-class "send code, not data" workflow: Metis writes a local analysis script, you run it on your machine, and only derived metadata (schema, value counts, summaries, model output) comes back. Available in Claude Code and Claude Desktop.
Data Guardian hardening — the pipeline PII scanner now runs all 11 patterns (names, DOB, passport, MRN, HAT/PNLTHA case numbers, DRC national ID, + the original five) through one shared scanner used by both the tool and the pipeline, so they can't drift; covered by a unit-test suite.
Pre-tool data-file guard — before a `Read`/`read_file`, the security hook peeks at the file's header locally and asks for confirmation before individual-level data is loaded into the conversation.
Honest positioning — dropped the "local-first/local AI" framing (reasoning runs on the Claude API); copy now states plainly that your data stays on your machine while reasoning uses Claude.
Desktop project-tracking + file-tracking fixes — the Desktop router now registers tracked projects; repaired a recursion bug that had broken file tracking.

Post-v1.0 — May 2026

What changed
Unified project intelligence system — unlimited projects with categories and folder paths in all installer paths; CLAUDE.md written to each project folder; Claude Desktop auto-registration; activity scanner detects git commits, modified files, and todo completions; Claude Code stop hook reports active project to dashboard
Three-path intelligent setup wizard — browser wizard (unlimited projects, categories), terminal wizard (Linux/macOS), and Inno Setup wizard (Windows .exe) all backed by Claude API persona generation
Docker test matrix — Ubuntu 24/22 + Debian + light profile running in parallel; mandatory pre-release gate in Release Coordinator
Today surface restructure — session handoff strip, 7-metric ledger, three-tier priority queue, 2×2 research quadrant layout, time-of-day adaptive morning brief
Metis real subagent orchestration — Metis spawns real isolated subagents via the Agent tool, with independent token tracking
Release Coordinator — proactive git guardian with `status` / `commit` / `push` / `audit` / `test-containers` commands
Scheduler fix — library index job corrected (scan_literature_folder in content_scan module)
Knowledge surface — unified search, coverage gap analysis, knowledge layer browser

v1.0 — May 2026

First stable release. See system/config/release-notes-v1.0.md for full details.

What shipped
FastAPI + HTMX dashboard — 9 tabs
34 specialist agents
MCP server — 170+ registered tools
Windows installer (Inno Setup)
Statistics for Epidemiology course — 12 lessons with spaced repetition
Startup eval suite + news freshness check
Auto-handoff brief at 80% context
AGPL-3.0 license

Earlier development (Phases 0–9b)

Phase	What shipped
0–5	MCP server, 34 agents, CLI skills, config wizard, SQLite (46 tables), 5-layer memory, knowledge graph, Zotero/Mendeley sync
6–7	FastAPI + HTMX dashboard — 9 tabs, live partials
8	Morning brief, news rail, meeting assistant, voice capture, PaperQA2 PDF search, cross-pollination, token guardrails
9	CSS design overhaul — editorial layout, responsive grid, animation
9b	Self-improvement loop — reflexion aggregation, proposal drafting, approval flow
M	Conversation memory — session summaries in episodic memory, semantic search across past sessions

License

AGPL-3.0 for the codebase — use, modify, and fork freely, but any version you run as a service or distribute must also be open-source under AGPL-3.0.

CC-BY-SA 4.0 for course content and learning materials.