StaffDeck

News

• 2026-09-01: We present v0.5.0 for faster and more stable execution.
• 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, NEU-ModelBest Data Intelligence Joint Lab, THUNLP, OpenBMB, and 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 or download the latest desktop release directly:

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:

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:

…

Table of Contents

• About StaffDeck
  • Core Features
  • Desktop Downloads
  • Agent-Friendly Quick Deploy
  • Table of Contents
  • Quick Start
    • Requirements
    • 1. Clone and Install
    • 2. Configure a Model
    • 3. Launch the Web Demo
    • 4. Verify the Installation
    • Useful Commands
      • Unified Python Entry
  • Core Workflows
  • Project Structure
  • FAQ
  • Roadmap
• Community
  • Contributing
  • Risks and Limitations
  • Citation
  • License
  • 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:

git clone https://github.com/OpenBMB/StaffDeck.git
cd StaffDeck

On macOS, Linux, or WSL:

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:

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:

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

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:

curl http://127.0.0.1:5173/api/health

On Windows PowerShell:

curl.exe http://127.0.0.1:5173/api/health

Expected output:

{"status":"ok"}

Open 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

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:

Replace up --detach with another lifecycle argument when needed:

Full guide → StaffDeck Tutorial

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 , /当前, /帮助;
• Intent auto-routing: each message is classified by an LLM and routed to the best-matching employee (stricter threshold during SOPs; sticky during human handoff and after manual switches);
• Identity merge: channel users run /绑定 to merge their channel identity into an existing StaffDeck account (memory and sessions unified; /解绑 to revert);
• Conversation history and delivery logs grouped by day with pagination; admins and employee creators can review all channel conversations per permission;
• Reliability: inbound idempotency, crash recovery, outbound retry with backoff, token-expiry alerts, and WeChat session self-healing.

WeChat (personal, iLink protocol)

1. Open "渠道接入" (Channel Integration) in the sidebar → "接入渠道" → choose "微信" and pick a default employee;
2. Click "扫码接入" and scan with WeChat (QR expires in ~2 minutes and auto-refreshes);
3. Message the bound WeChat account (DM or group) to chat.

WeCom (bot WebSocket)

1. WeCom admin console → Apps → Bot, create a bot and copy its Bot ID and Secret;
2. "接入渠道" → choose "企业微信", pick a default employee and create;
3. Enter Bot ID and Secret (optionally fill in 企业 ID/Corp ID to distinguish duplicate userids across enterprises — recommended);
4. The long connection is established automatically; once it shows "已连接", messages flow.

Production checklist

• Set a strong random APP_SECRET (channel credentials encryption derives from it; better to also set an independent CHANNEL_SECRET);
• Channel credentials (bot tokens/secrets) are stored Fernet-encrypted and never returned by any API;
• Binding management is restricted to admins or the binding creator; mounting an employee exposes it to all users of that channel — grant with care.

Project Structure

StaffDeck/
├── backend/                  # FastAPI APIs, agent runtime, storage, and task workers
├── frontend-enterprise/      # React/TypeScript StaffDeck worksp