OpenBMB/StaffDeck

OpenBMB/StaffDeck

Description: Enterprise Digital Employee Platform

Language: Python

Stars: 8

Forks: 3

Open issues: 0

Created: 2026-07-13T03:31:47Z

Pushed: 2026-07-16T06:56:10Z

Default branch: main

Fork: no

Archived: no

README:

News

• 📌 Pinned · 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.

Agent-Friendly Quick Deploy

Paste the prompt below into Cursor, Claude Code, or Codex:

Read https://raw.githubusercontent.com/OpenBMB/StaffDeck/main/README.md.
Clone the OpenBMB/StaffDeck repository, prepare Python 3.11 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, run DETACH=1 scripts/dev_up.sh, and
verify /api/health plus /workspace/gallery before reporting success.

Table of Contents

• [💡 About StaffDeck](#-about-staffdeck)
• [Core Features](#core-features)
• [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)
• [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, or WSL when using the development scripts
• 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

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

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

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

DETACH=1 scripts/dev_up.sh

The script 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

curl 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

scripts/dev_status.sh # inspect service status
scripts/dev_down.sh # stop the local service
scripts/dev_up.sh # run in the foreground

> 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...