⚙️

Agent工作流

PilotDeck

基于 TypeScript · 无代码搭建完整 AI 自动化流程

⭐ 2.7k Stars 🍴 251 Forks 💻 TypeScript 📄 AGPL-3.0 🏷 AI 8.0分

8.0AI 综合评分

AI工作流TypeScript

📚 深度解析

PilotDeck 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升，基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化（模拟鼠标键盘操作），AI Agent 工作流通过理解任务意图、动态规划执行路径，能够处理更复杂的非结构化任务。

PilotDeck 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。

在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.0 分，是同类 Agent 工作流中的精选推荐。

📋 工具概览

PilotDeck 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

GitHub Stars

⭐ 2.7k

开发语言

TypeScript

支持平台

Windows / macOS / Linux

维护状态

持续维护，定期更新

开源协议

AGPL-3.0

AI 综合评分

8.0 分

工具类型

Agent工作流

Forks

251

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

PilotDeck 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

📌 核心特色

可视化 Agent 工作流编排，无需编写复杂代码
支持多步骤自动化任务链，实现全流程无人值守
与外部 API、数据库和第三方服务无缝集成
内置错误处理与自动重试机制，保障稳定运行
提供可复用的自动化模板，快速在同类场景部署

🎯 主要使用场景

自动化日常重复性工作，将精力集中于创造性任务
构建数据采集 → 处理 → 输出的完整自动化管线
实现跨平台、跨系统的数据流转和业务协同

以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。

安装命令

# 方式一：npm 全局安装
npm install -g pilotdeck

# 方式二：npx 直接运行（无需安装）
npx pilotdeck --help

# 方式三：项目依赖安装
npm install pilotdeck

# 方式四：从源码运行
git clone https://github.com/OpenBMB/PilotDeck
cd PilotDeck
npm install
npm start

📋 安装步骤说明

访问 GitHub 仓库获取工作流文件
在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
上传工作流文件
按照提示配置必要的环境变量和 API Key
运行测试确认流程正常后投入使用

以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。

常用命令 / 代码示例

# 命令行使用
pilotdeck --help

# 基本用法
pilotdeck [options] <input>

# Node.js 代码中使用
const pilotdeck = require('pilotdeck');

const result = await pilotdeck.run(options);
console.log(result);

以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。

配置示例

# pilotdeck 配置说明
# 查看配置选项
pilotdeck --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量（覆盖配置文件）
export PILOTDECK_CONFIG="/path/to/config.yml"

📑 README 深度解析真实文档完整度 69/100 查看 GitHub 原文 →

以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

简介

Task-oriented AI Agent productivity platform — redefining operational boundaries and memory evolution, one WorkSpace at a time.

English | <a href="./README.zh.md">简体中文</a> <a href="https://pilotdeck.openbmb.cn">Website</a> · <a href="https://pilotdeck.openbmb.cn/pilotdeck.github.io/demo/p/pilotdeck-demo">Live Demo</a> · <a href="https://pilotdeck.openbmb.cn/pilotdeck.github.io/docs/en/introduction">Tutorial</a> · <a href="#-installation--quick-start">Quick Start</a> · <a href="#-key-highlights">Highlights</a> · <a href="#use-cases">Use Cases</a> · <a href="#-community">Community</a>

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News 🔥

[2026.05.28] PilotDeck is now open source! Visit our official website at pilotdeck.openbmb.cn. We welcome contributions, feedback, and stars from the community.

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💡 About PilotDeck

PilotDeck is an open-source agent operating system designed around the concept of "WorkSpace". It is jointly developed and open-sourced by Tsinghua University THUNLP, ModelBest, OpenBMB, and AI9Stars. Targeting general-purpose, multi-task scenarios, PilotDeck is built to be a true productivity tool for the Agent era.

A wave of excellent AI Agent harnesses has emerged in recent years, each with its own focus: Claude Code / Cursor / Trae Solo brought model reasoning deep into the programming IDE; Claude Cowork introduced the notion of project-level isolation to desktop-side knowledge work; WorkBuddy connected agents to IM ecosystems such as WeCom and Feishu so AI is one message away.

When we shift the lens from "one-shot programming" or "immediate Q&A" to long-running, multi-project productivity work, however, several questions remain open:

When many projects run in parallel, can memory be white-box and traceable? When the AI gets something wrong, can you pinpoint which memory entry caused it and edit it directly — without starting a new chat from scratch?
Can token cost be tracked per task, so that running agents in the background actually becomes economically viable?
Can tasks of different difficulty automatically be matched to different models, instead of burning the flagship model on trivial calls?
When you step away from the keyboard, can the work keep moving? Can the agent proactively discover what's worth doing, report progress, and land results as files on disk?

PilotDeck is an incremental exploration around exactly these questions. It uses the WorkSpace as the fundamental unit — completely isolating files, memory and skills per project — and pairs it with three pillar capabilities: White-box Memory, Smart Routing and Always-on. The entire system natively supports the Model Context Protocol (MCP) and behaves consistently across front-ends (Web / CLI / IM).

✨ Key Highlights

WorkSpace-Level Isolation & Accretion

Every project gets its own file system, memory store and skill set. Parallel work no longer interferes with itself, retrieval has a bounded scope, and skills accrete naturally as each task grows — no more global context pollution.

</td> <td width="50%" valign="top">

Traceable White-box Memory

Memory generation, extraction, storage and retrieval are visible end-to-end. When the AI mis-remembers, you can pinpoint and fix the offending entry. Built-in Dream Mode consolidates memory in idle windows, and supports one-click rollback.

</td> </tr> <tr> <td width="50%" valign="top">

Smart Routing & Cost Optimization

Task difficulty is auto-detected; complex calls go to flagship models (e.g. Claude 3.5 Sonnet / GPT-4o), simple ones drop to lighter models. Through on-device / cloud co-orchestration and precise matching, token spend shrinks dramatically without sacrificing quality.

</td> <td width="50%" valign="top">

Always-on Background Execution

PilotDeck breaks the "you ask, it answers" loop: after you sign off, the agent keeps discovering candidate tasks, running long-horizon monitors, and finally lands deliverables as local files with a summary report waiting for you.

</td> </tr> </table>

📦 Installation & Quick Start

We provide a one-line installer for macOS / Linux, plus a source-based workflow for developers.

Option A: One-line install (recommended, macOS / Linux)

curl -fsSL https://raw.githubusercontent.com/OpenBMB/PilotDeck/main/install.sh | bash

The script auto-installs Node.js 22, clones the repo, installs dependencies, and builds the frontend. Once it finishes:

pilotdeck            # starts the server at http://localhost:3001
pilotdeck status     # check runtime status

Option C: Docker Compose

If Docker is installed, you can start PilotDeck with:

docker compose up -d

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Use Cases

All demos below are generated entirely by edge-side models via PilotDeck's Smart Routing — no cloud-side frontier model required.

Work Document Generation

"Survey the Chinese LLM application market and turn it into a formal HTML white paper."

Process	Result

Mini-Game Development

"Walk me through building an iOS AR mini-game Ball Finder in Vibe Coding mode."

Process	Result

AI Engineering Platform Development

"Build a low-code embedding fine-tuning platform from scratch."

Process	Result

Audio-Video Editing & Social Media Operations

"Push this English podcast to a global audience in Chinese / Japanese / French / Korean / Spanish / Arabic."

Process Result (with audio)

https://github.com/user-attachments/assets/a7245467-ee3c-4939-a055-c56576ac56d1

</td> </tr> </table>

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🖥️ UI & Demo

PilotDeck ships an out-of-the-box Web UI with full WorkSpace management, white-box memory editing, and visualization of multi-agent collaboration.

Option B: From source (for developers)

1. Clone and install dependencies

This repo uses Git LFS for large media assets. Make sure git lfs is installed before cloning. If you don't need the demo videos/GIFs, add GIT_LFS_SKIP_SMUDGE=1 before git clone to skip downloading them.

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

npm install              # root deps (Gateway runtime)
cd ui && npm install     # UI deps
cd ..

2. Configure a model provider

PilotDeck reads ~/.pilotdeck/pilotdeck.yaml. You can create it manually, let the bootstrap script generate one, or just open the Web UI and configure providers visually in the settings panel. Supported protocols include OpenAI, Anthropic, DeepSeek, Qwen, Kimi, MiniMax and other OpenAI-compatible endpoints.

schemaVersion: 1
agent:
  model: deepseek/deepseek-v4-pro
model:
  providers:
    deepseek:
      protocol: openai
      url: https://api.deepseek.com/v1
      apiKey: sk-your-api-key

3. Start the services

```bash cd ui && npm run dev # dev mode (HMR), visit http://localhost:5173

🛠️ Extension Protocol

PilotDeck has an open plugin architecture with a strict boundary between the open-source core and plugin customization. Extending the system is a plugin.json away:

MCP Servers — first-class integration with any Model Context Protocol server.
Tools & Skills — register custom tools, or pull community skills via ClawHub.
Lifecycle Hooks — intercept PreToolUse, UserPromptSubmit, and other critical lifecycle events.
Custom Memory — plug in your own memory store provider.

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🎯 aiskill88 AI 点评 A 级 2026-06-01

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