## News
- **2026-08-18**: We present v0.4.0 for multi-staff cooperation and faster runing.
- **2026-08-06**: We present v0.3.0 for SOP editing and sandbox.
- **2026-08-03**: We present v0.2.0 for harnessv2 and IM system.
- **2026-07-15**: StaffDeck is now open source! We welcome your feedback and support with a Star.
# 💡 About StaffDeck
StaffDeck is an enterprise platform for building and managing digital employees. It helps professionals turn their work experience, business processes, and decision criteria into digital employees that can operate continuously, take over repetitive tasks, and preserve individual expertise as reusable, evolvable, and traceable organizational assets. StaffDeck is jointly developed by the [ModelBest](https://modelbest.cn/), [NEU-ModelBest Data Intelligence Joint Lab](https://neuir.github.io/), [THUNLP](https://nlp.csai.tsinghua.edu.cn/), [OpenBMB](https://www.openbmb.cn/home), and [AI9Stars](https://github.com/AI9Stars) for enterprises and institutions seeking to advance AI from a personal productivity tool to an organizational capability.
## Core Features
- 🧑💼 **Build and manage digital employees**: Turn professional experience, processes, and decision criteria into digital employees with positions, employee IDs, capability profiles, and work records. Support capability growth, permission isolation, publishing, and reuse.
- 🧩 **State-machine-driven procedural skills**: Generate structured SOPs from natural language and use state machines to execute complex processes accurately. Support real-time switching across multiple flows, context preservation, visual editing, version management, and branch evolution.
- 📚 **Document-structure-aware knowledge retrieval**: Build navigable indexes across documents, chapters, pages, summaries, and other levels, allowing digital employees to first estimate where information may reside and then locate the original text step by step. Support knowledge buckets, targeted retrieval, source citations, and retrieval debugging.
- 🔌 **Autonomous execution and continuous improvement**: Perform real business operations through HTTP APIs, MCP, and scheduled tasks, then close the improvement loop with long-term memory, complete traces, human takeover, user feedback, and feedback analysis.
## Desktop Downloads
Visit the [StaffDeck official website](https://staffdeck.openbmb.cn/) or download the latest desktop release directly:
| Platform | Architecture | Download |
| --- | --- | --- |
| macOS | Apple Silicon (arm64) | [Download `.dmg`](https://github.com/OpenBMB/StaffDeck/releases/latest/download/StaffDeck-macos-arm64.dmg) |
| macOS | Intel (x86_64) | [Download `.dmg`](https://github.com/OpenBMB/StaffDeck/releases/latest/download/StaffDeck-macos-x86_64.dmg) |
| Windows | x64 | [Download installer `.exe`](https://github.com/OpenBMB/StaffDeck/releases/latest/download/StaffDeck-windows-x64-setup.exe) |
| Linux | x86_64 (Debian/Ubuntu) | [Download `.deb`](https://github.com/OpenBMB/StaffDeck/releases/latest/download/StaffDeck-linux-x86_64.deb) |
Linux packages listen on `127.0.0.1` by default. Use `staffdeck setup` from a
terminal to choose the listening mode and port, including on a headless host:
```bash
staffdeck setup
staffdeck setup --mode local --port 5173
staffdeck setup --mode lan --port 5173
staffdeck setup --mode public --port 5173 --public-url https://staff.example.com
```
`local` listens only on the machine; `lan` and `public` listen on `0.0.0.0`.
`--port` sets the listening port. In interactive terminals, `public` tries to
infer a public URL first; if it cannot, or when running headless, pass
`--public-url` explicitly. For public deployments, use an HTTPS reverse proxy
and set the same public URL as `OIDC_REDIRECT_URI` when SSO is enabled. The
setup is saved per user and applied on the next launch.
## Agent-Friendly Quick Deploy
Paste the prompt below into Cursor, Claude Code, or Codex. For code-based
deployments, you can also override the launch at runtime with
`ULTRARAG_HOST`, `ULTRARAG_PORT`, and `STAFFDECK_PUBLIC_URL`:
```text
Read https://raw.githubusercontent.com/OpenBMB/StaffDeck/main/README.md.
Clone the OpenBMB/StaffDeck repository, prepare Python 3.11 or newer and Node.js 20,
create backend/.venv, install the backend and frontend dependencies, copy
backend/.env.example to backend/.env, ask me for the OpenAI-compatible model
endpoint and API key if they are missing, and use the commands documented for
the current OS. Start with scripts/dev_up.sh --detach on macOS/Linux/WSL or
.\scripts\dev_up.ps1 --detach on Windows PowerShell, then verify /api/health
plus /workspace/gallery before reporting success.
```
## Table of Contents
- [💡 About StaffDeck](#-about-staffdeck)
- [Core Features](#core-features)
- [Desktop Downloads](#desktop-downloads)
- [Agent-Friendly Quick Deploy](#agent-friendly-quick-deploy)
- [Table of Contents](#table-of-contents)
- [Quick Start](#quick-start)
- [Requirements](#requirements)
- [1. Clone and Install](#1-clone-and-install)
- [2. Configure a Model](#2-configure-a-model)
- [3. Launch the Web Demo](#3-launch-the-web-demo)
- [4. Verify the Installation](#4-verify-the-installation)
- [Useful Commands](#useful-commands)
- [Unified Python Entry](#unified-python-entry)
- [Core Workflows](#core-workflows)
- [Project Structure](#project-structure)
- [FAQ](#faq)
- [Roadmap](#roadmap)
- [💬 Community](#-community)
- [Contributing](#contributing)
- [Risks and Limitations](#risks-and-limitations)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## Quick Start
### Requirements
- macOS, Linux, WSL, or Windows PowerShell
- Python **3.11+**
- Node.js **20+** and npm
- An OpenAI-compatible Chat Completions endpoint and API key
- No CUDA requirement for the application itself; hardware requirements depend on the selected model service
### 1. Clone and Install
Clone the repository first:
```bash
git clone https://github.com/OpenBMB/StaffDeck.git
cd StaffDeck
```
On macOS, Linux, or WSL:
```bash
python3 -m venv backend/.venv
backend/.venv/bin/python -m pip install -e "backend[dev]"
npm --prefix frontend-enterprise ci
cp backend/.env.example backend/.env
```
On Windows PowerShell:
```powershell
py -3 -m venv backend\.venv
.\backend\.venv\Scripts\python.exe -m pip install -e "backend[dev]"
npm --prefix frontend-enterprise ci
Copy-Item backend\.env.example backend\.env
```
### 2. Configure a Model
Edit `backend/.env` before the first startup:
```dotenv
APP_SECRET="replace-with-a-long-random-secret"
DEMO_MODEL_BASE_URL="https://your-openai-compatible-endpoint/v1"
DEMO_MODEL_NAME="your-model-name"
DEMO_MODEL_API_KEY="your-api-key"
```
The API key is used to create the initial model configuration and is encrypted before being stored in the database. Do not commit `backend/.env`. After startup, model services can also be managed from **Admin → Model Configuration**.
### 3. Launch the Web Demo
| Platform | Recommended command |
| --- | --- |
| macOS, Linux, or WSL | `scripts/dev_up.sh --detach` |
| Windows PowerShell | `.\scripts\dev_up.ps1 --detach` |
Both wrappers call the same cross-platform Python lifecycle entry, `scripts/dev.py`. The startup process builds the StaffDeck frontend and serves the UI, API, and Swagger documentation from one FastAPI process on port `5173`.
Initial administrator credentials: username `admin`, password `admin`. Please change the password after first login.
### 4. Verify the Installation
On macOS, Linux, or WSL:
```bash
curl http://127.0.0.1:5173/api/health
```
On Windows PowerShell:
```powershell
curl.exe http://127.0.0.1:5173/api/health
```
Expected output:
```json
{"status":"ok"}
```
Open [http://127.0.0.1:5173/workspace/gallery](http://127.0.0.1:5173/workspace/gallery), select a digital employee, and send the first message. The answer and its execution record should stream into the same conversation turn.
### Useful Commands
| Action | macOS, Linux, or WSL | Windows PowerShell |
| --- | --- | --- |
| Start in the background | `scripts/dev_up.sh --detach` | `.\scripts\dev_up.ps1 --detach` |
| Start in the foreground | `scripts/dev_up.sh` | `.\scripts\dev_up.ps1` |
| Inspect service status | `scripts/dev_status.sh` | `.\scripts\dev_status.ps1` |
| Stop the local service | `scripts/dev_down.sh` | `.\scripts\dev_down.ps1` |
#### Unified Python Entry
The wrapper scripts above delegate to `scripts/dev.py`. It can also be called directly with the project virtual environment created in step 1, avoiding any dependency on shell script execution or a system Python launcher:
| Platform | Direct background start |
| --- | --- |
| macOS, Linux, or WSL | `backend/.venv/bin/python scripts/dev.py up --detach` |
| Windows PowerShell | `.\backend\.venv\Scripts\python.exe scripts\dev.py up --detach` |
Replace `up --detach` with another lifecycle argument when needed:
| Action | Arguments |
| --- | --- |
| Start in the background | `up --detach` |
| Start in the foreground | `up` |
| Inspect service status | `status` |
| Stop the local service | `down` |
> Full guide → [StaffDeck Tutorial](https://staffdeck.openbmb.cn/#/docs/introduce?lang=en)
## Core Workflows
1. **Create a digital employee**: Define the position, role boundaries, service style, creator, and access scope.
2. **Configure employee capabilities**: Copy from the marketplace or create knowledge bases, general skills, SOPs, and tools without modifying marketplace originals.
3. **Start a session**: Enter from the digital employee marketplace or employee list; the formal session is persisted after the first message is sent.
4. **Execute and observe**: Inspect streaming intent, retrieval, skill, tool, review, and response events in the execution record.
5. **Intervene when necessary**: Continue with queued requests, cancel a run, hand work to a person, or process pending answers.
6. **Operate continuously**: Improve employee capabilities over time through memory, feedback, conversation logs, and scheduled tasks.
## Channel Integration (WeChat / WeCom)
Digital employees can serve users directly over IM channels: users chat with employees in WeChat or WeCom, while multi-agent dispatch, intent-based auto routing, identity merging, and conversation observability are built into the platform. The channel kernel is channel-agnostic (adapter registry) — new channels only need a new adapter.
**Capabilities**
- Mount multiple digital employees on one channel account; dispatch with `/员工`, `/切换 
