Staffwright — Your Small Team's AI Staff, Self-Hosted

Staffwright turns one ordinary computer into your team's AI workstation: shared long-term memory, a company knowledge base, document search, and a live office view of your AI at work. Deploy once — every phone, laptop and tablet on your network reaches it from the browser. No app installs. Up to ~20 people. MIT-licensed.

(staff + wright, as in "playwright" — a maker of staff.)

• 🧠 Shared memory — cross-session long-term memory, exposed through the official MCP interface and shared by the whole team

• 📚 Knowledge base — ingest and search your docs anytime: industry material, SOPs, research notes

• 🌐 Every device on the network — deploy once on one always-on machine; desktops, phones and tablets all connect from the browser

• 🏠 Your hardware — data stays on your server and never leaves your LAN

• 🖥️ Virtual office — a pixel-art, view-only visualization of your AI staff at work

Under the hood, Staffwright runs the official DeepSeek Harness (DSH) engine untouched — zero source changes, all integration through DSH's standard MCP interface. Staffwright is the standalone, international-first distribution of the deepDDW project; the DSH-plugin line continues at ccch713/deepddw.

⚠️ Read Before Installing

Staffwright implements its own memory and knowledge-base isolation. To avoid conflicts or memory confusion:

1. Back up your existing memory and knowledge-base contents (wherever they live in your DSH setup);

2. Uninstall/disable any other memory or knowledge-base plugins;

3. Only then install Staffwright and let it own the memory & knowledge-base stack.

Running it alongside other memory/KB plugins may cause data conflicts or memory mixing between namespaces.

Related MCP server: Lore

Try It in 60 Seconds

# 1. On the always-on machine (macOS 14+ / Ubuntu 22+):
git clone https://github.com/ccch713/staffwright.git && cd staffwright
./install.sh --with-dsh        # installs the DSH engine automatically if missing,
                               # then the gateway + DSH MCP bridge
./install.sh --port 8600       # 2. start (runs the test suite first, then serves)

# 3. From any device on the same network:
#    http://<server-ip>:8600/   → the DSH workbench, team-wide
#    Phones/tablets: "Add to Home Screen" for an App-like experience

# 4. Add your API key in DSH Settings → Models (see "Do I need an API key?" below)
# 5. In chat, ask the model to "search the knowledge base" or "remember …"
#    → it auto-invokes the built-in MCP tools

Requirements: one ordinary computer/server (8 GB RAM minimum, 16 GB+ recommended), Python 3.11+, no GPU needed (LLM via cloud API or local Ollama).

Do I Need an API Key?

Staffwright ships no keys, no accounts, no lock-in — you pick one of three ways to give your staff a brain. Nothing else needs configuring: the gateway, memory, knowledge base and document search work out of the box; the key only powers the chat model.

Then hire your first staff member in ~5 minutes from roles/ — see below.

Prefer Docker?

cp .env.example .env    # set DDW_ACCESS_TOKEN
docker compose -f deepddw-compose.yml up -d --build
# → http://<host>:8500/

Windows (standalone exe)

A Windows build is produced automatically by CI — download deepddw-windows.zip (legacy artifact name) from the latest Actions → Artifacts:

deepddw-windows/deepddw.exe          # unzip anywhere, no Python/Node needed
# optional: set DDW_DATA_DIR / DDW_ACCESS_TOKEN
# starts on 0.0.0.0:8500 → open http://<host>:8500/health

Upgrades: replace the folder with a newer zip — your data (under %USERPROFILE%\.deepddw) is untouched. See docs/windows-packaging.md.

Why Staffwright?

Most AI-assistant setups stop at "chat in a browser tab". Staffwright is a complete workstation:

In one sentence: turn a "personal AI toy" into a tool a small team actually runs its daily AI workflow on — fully packaged, easy to deploy, low maintenance, up to ~20 people.

Rule of thumb: one instance per team of ≤20 people on one network. If your scenario fits in one room, one floor, or one VPN, Staffwright fits you.

What's Inside

Ready-Made AI Staff — Pick a Role, Not a Blank Prompt

Installing is not the finish line; a new AI hire needs a job description. Staffwright ships role packs distilled from real small-business deployments, so a new team goes install → pick a role → useful in under an hour:

The field-tested deployments behind these packs — dental clinic, e-commerce shop, real-estate agency — are documented in docs/playbooks.md.

How It Works

📱 Phone / 💻 Desktop / 📱 Tablet / 🖥️ Laptop — any device on the network
   │                  (browser access, no app install)
   ▼
Staffwright Gateway (one server on your network)
   ├─ /dsh/*     proxy → DSH engine (official UI, model config & chat untouched)
   ├─ /api/*     proxy → DSH RPC/API
   └─ /api/v1/*  Staffwright capabilities: Knowledge Base / Memory / Docs / LLM config
        │
        │  DSH official MCP client (streamable-http)
        ▼
Staffwright MCP tools (auto-invoked by the model)
   ├─ mcp__deepddw__ddw_kb_search           knowledge base search
   ├─ mcp__deepddw__ddw_memory_put          write memory
   ├─ mcp__deepddw__ddw_memory_search       search memory
   └─ mcp__deepddw__ddw_docs_portal_search  document search

Integration = DSH standard MCP — zero intrusion, zero changes to DSH source. (Tool names keep the internal deepddw codename; a rename pass is on the roadmap.)

Virtual Office

Open /ui/office.html and watch your Staffwright at work: every subsystem is a pixel-art staff member — the gateway doorman waves devices in, the chat receptionist types away, the memory butler files notes, the librarian shelves new docs, the meter clerk bills LLM calls. Real activity drives the animations (no fake loops: 30 minutes without events → back to idle); failures raise a banner; the day's digest is importance-weighted. Fully programmatic rendering — zero third-party art assets, zero runtime dependencies. View-only by design: the API exposes nothing but job titles and whitelisted count phrases — never document contents, memory values, or chat transcripts.

Security & Privacy

Roadmap

Only items actually planned or already delivered are listed.

Delivered highlights (full history in git log):

• Multi-device on LAN — device registry, status panel, rate limiting, SQLite WAL concurrency (up to 20 devices, load-tested: P95 ≤ 126 ms, 0% errors)

• Workspace isolation · cross-device session resume · optional TLS · backup/restore API · version/upgrade check

• Docker one-command deployment (verified end-to-end) · Windows exe via CI

• Session → document auto-ingest · hybrid vector search · memory relevance ranking

• Organization binding (optional bindcode-v1 plugin) — act as the enterprise backend for assistant apps; HMAC-signed short-lived bind codes; member tokens never touch the admin API

• Virtual office (view-only activity visualization)

Planned:

• Scheduled staff routines — cron-driven roles (watch this space)

• One-line curl … | bash installer

• Full English documentation pass (some docs/UI strings still reference the legacy deepddw codename)

• Managed cloud (overseas regions)

• Optional managed relay for model tokens — zero-config keys for teams who want them; strictly opt-in, BYO-key stays the default

• Community Discord

Commercial & Support

Staffwright is free and fully featured forever. Open source is the product — there are no feature gates and no "Pro-only" paywalls in this repository.

Paid convenience tiers (managed cloud hosting, enterprise collaboration: SSO, audit logs, private-deployment support) are being prepared for international availability. In the meantime: questions, deployment help and enterprise inquiries → GitHub Discussions or open an issue.

Community

• 💬 Discussions — questions, show & tell, deployment help

• 🐛 Issues — bug reports & feature requests

• 🤝 Contributing: see CONTRIBUTING.md

License

See NOTICE for full third-party attribution.