WonderLab

Posted on Mar 16

Open Source Project of the Day (Part 18): OpenWork - Open-Source Claude Cowork, Local AI Agent Workbench

#openwork #coworks #ai #agents

Introduction

"What if Claude Cowork could run open-source and locally?"

This is Part 18 of the "Open Source Project of the Day" series. Today we explore OpenWork (GitHub).

In the AI agent tool space, Claude Cowork is a powerful collaborative workbench — but it's a closed-source commercial product. OpenWork fills this gap: it's a fully open-source desktop application powered by OpenCode CLI that implements functionality similar to Claude Cowork: running AI agents, skills, and MCP (Model Context Protocol). More importantly, it adopts a "local-first, cloud-ready" design philosophy, letting you run it on your own machine with one click while maintaining complete control over your AI workflows.

Why this project?

🖥️ Open-source alternative: A free, open-source alternative to Claude Cowork
🏠 Local-first: Run AI agents locally with complete data ownership
🔌 MCP protocol support: Native support for Model Context Protocol
🧩 Composable architecture: Freely switch between desktop app, chat app connector, and server modes
⚡ Skill manager: Install, import, and manage various skills
🌐 Cross-platform support: Built with Tauri, supports macOS, Windows, Linux
🔓 "Ejectable" design: Can even work via command line without any UI

What You'll Learn

OpenWork's core architecture and design philosophy
How to deploy and run AI agent workflows locally
MCP protocol applications in OpenWork
Usage and management of the skill system
Differences between Host Mode and Client Mode and their use cases
Comparative analysis with other AI workbench tools
Real-world application scenarios and best practices

Prerequisites

Basic understanding of AI agent concepts
Familiarity with MCP (Model Context Protocol) (optional)
Command-line experience (optional)
Basic understanding of desktop application development (optional)

Project Background

Project Introduction

OpenWork is an open-source desktop AI agent workbench that serves as an alternative to Claude Cowork, with OpenCode CLI providing core capabilities. It allows users to run AI agents, skills, and MCP locally or on a server, supporting scaling from single-user to team collaboration. OpenWork adopts a "local-first, cloud-ready" design philosophy, letting users run AI workflows locally with one click while also supporting connections to remote servers.

Core problems the project solves:

Claude Cowork is a closed-source commercial product that cannot be locally deployed
Existing AI agent tools lack unified skill management and MCP support
Team collaboration requires shared AI workflows but lacks open-source solutions
Developers need an extensible, composable AI agent workbench
Privacy-sensitive scenarios require fully localized AI agent environments

Target user groups:

Developers who need to run AI agents locally
Users who want Claude Cowork-like functionality without a subscription
Technical users interested in MCP protocol
Teams that need to share AI workflows
Enterprises and individuals with high data privacy requirements

Author/Team Introduction

Team: different-ai

Background: A team focused on open-source AI tool development
Notable projects: OpenWork, file-organizer-2000, and more
Philosophy: Make AI tools more open, accessible, and composable
Tech stack: TypeScript, Rust, Tauri, React

Project Stats

⭐ GitHub Stars: 9.2k+ (rapidly growing)
🍴 Forks: 768+
📦 Version: Continuously updated (830+ commits)
📄 License: MIT (fully open source, free to use)
🌐 Project address: GitHub
💬 Community: Active GitHub Issues
👥 Contributors: 23 contributors with active community participation

Project development history:

2024-2025: Project created, core functionality implemented
Continuous iteration: Added Skill Manager, MCP support, and more features
Community growth: Reached 9.2k+ Stars, became a popular project
Multi-modal support: Supports desktop app, web interface, CLI, and more

Main Features

Core Purpose

OpenWork's core purpose is to provide an open-source AI agent workbench, with main features including:

AI agent execution: Run various AI agents locally or on a server
Skill management: Install, import, and manage various skills
MCP protocol support: Native support for Model Context Protocol
Session management: Manage multiple sessions, supports real-time prompt sending
Workflow templates: Save workflow templates for repeated execution
Permission control: Handles agent permission requests
Execution timeline: Visually displays the execution plan timeline
Event stream subscription: Supports SSE event stream subscriptions

Use Cases

OpenWork is suitable for a variety of AI workflow scenarios:

Local development
- Run AI agents locally for development and testing
- Use full functionality without cloud services
- Protect code and data privacy
Team collaboration
- Set up a shared AI agent server
- Team members connect via Client Mode
- Unified management of skills and workflows
Automated tasks
- Use skills to automate repetitive tasks
- Save workflow templates for repeated execution
- Batch process multiple tasks
MCP integration
- Connect to various MCP servers
- Extend AI agent capabilities
- Integrate with existing toolchains

Quick Start

Installation

OpenWork offers multiple installation methods:

Method 1: Download pre-built binaries (recommended)

# macOS users
# Visit the Releases page to download the DMG file

# Or visit the official page to download
# https://github.com/different-ai/openwork/releases

Method 2: Build from source

# 1. Clone the repository
git clone https://github.com/different-ai/openwork.git
cd openwork

# 2. Install dependencies
pnpm install

# 3. Start development server
pnpm dev       # desktop app
pnpm dev:ui    # web interface only

System requirements:

Node.js 18+
pnpm package manager
Rust (if building Tauri from source)
macOS / Windows / Linux

Basic Usage

Start the application:

# Development mode
pnpm dev

# Production build
pnpm build

Choose connection mode:

OpenWork supports two running modes:

Host Mode
- Start a complete service locally
- Suitable for individual use or as a team server
Client Mode
- Connect to a remote OpenWork server
- Suitable for team collaboration scenarios

Common Operation Examples

# Start desktop app (development mode)
pnpm dev

# Start web interface only
pnpm dev:ui

# Build desktop app
pnpm build

# Use CLI mode (openwrk)
# openwrk provides a pure command-line host mode

Core Features

OpenWork's core features include:

Local-first design
- One-click run on local machine
- Data completely under user control
- No cloud services required
Composable architecture
- Desktop app mode
- Chat app connector (e.g., WhatsApp)
- Server mode
"Ejectable" design
- Powered by OpenCode
- Can work even without a UI
- Supports pure CLI command-line operation
Session management
- Create and manage multiple sessions
- Send prompts in real time
- View session history
SSE event stream
- Subscribe to real-time events
- Track execution progress
- Receive agent responses
Execution plan timeline
- Visually displays execution steps
- Track agent behavior
- Debug and analyze
Permission request handling
- Agent permission request popups
- User authorization control
- Secure execution guarantee
Workflow templates
- Save common workflows
- One-click repeated execution
- Team-shared templates
Skill manager
- Install new skills
- Import custom skills
- Manage installed skills

Project Advantages

Compared to other AI workbench tools, OpenWork's advantages:

Comparison	OpenWork	Claude Cowork	Other Open-Source Solutions
Open source level	Fully open source (MIT)	Closed-source commercial	Partially open source
Local execution	Natively supported	Primarily cloud	Partially supported
MCP support	Natively supported	Supported	Limited support
Skill system	Complete	Complete	Simple
Cross-platform	macOS/Win/Linux	Primarily web	Varies
Composability	High (multi-modal)	Medium	Low
Customization	High (source-modifiable)	Low	Medium
Price	Free	Paid subscription	Free/partially paid

Why choose OpenWork?

✅ Fully open source: MIT license, free to use and modify
✅ Local-first: Data privacy guaranteed
✅ Native MCP: Complete protocol support
✅ Multi-modal support: Desktop, web, CLI — your choice
✅ Skill ecosystem: Rich skill management capabilities
✅ Active community: 9.2k+ Stars, continuously updated

Detailed Project Analysis

Architecture Design

OpenWork uses Tauri + React architecture to implement a cross-platform desktop application:

┌─────────────────────────────────────────────────────────┐
│                    USER INTERFACE                        │
│  🖥️ Desktop App (Tauri Window)                          │
│  🌐 Web UI (React)                                      │
│  💬 Chat Connectors (WhatsApp, etc.)                   │
└─────────────────────────────────────────────────────────┘
                        │
                        ▼
┌─────────────────────────────────────────────────────────┐
│               APPLICATION LAYER                          │
│  📝 Session Management                                  │
│  🔧 Skill Manager                                       │
│  📊 Execution Timeline                                  │
│  🔐 Permission Handler                                  │
└─────────────────────────────────────────────────────────┘
                        │
                        ▼
┌─────────────────────────────────────────────────────────┐
│                 CORE ENGINE                              │
│  ⚙️ OpenCode CLI Integration                            │
│  📡 SSE Event Stream                                    │
│  🔌 MCP Protocol Handler                                │
└─────────────────────────────────────────────────────────┘
                        │
                        ▼
┌─────────────────────────────────────────────────────────┐
│              RUNTIME LAYER                               │
│  🦀 Rust/Tauri (Desktop Runtime)                        │
│  📦 Node.js (Server Runtime)                            │
└─────────────────────────────────────────────────────────┘
                        │
                        ▼
┌─────────────────────────────────────────────────────────┐
│                  INTEGRATIONS                            │
│  🤖 AI Providers (Claude, OpenAI, etc.)                │
│  🔌 MCP Servers                                         │
│  📁 Local File System                                   │
└─────────────────────────────────────────────────────────┘

Core components:

User interface layer: Tauri desktop window + React Web UI
Application layer: Session management, skill management, execution timeline, permission handling
Core engine: OpenCode CLI integration, SSE event stream, MCP protocol handling
Runtime layer: Rust/Tauri (desktop) + Node.js (server)
Integration layer: AI providers, MCP servers, local file system

Core Module Analysis

1. OpenCode Integration

OpenWork is powered by OpenCode CLI, providing core AI agent capabilities:

Functions:

Execute AI agent tasks
Manage agent lifecycle
Handle agent inputs and outputs

Features:

Can run independently without UI
Supports direct command-line invocation
Provides complete agent capabilities

2. Skill Manager

OpenWork provides complete skill management functionality:

Functions:

Browse and install new skills
Import custom skills
Manage installed skills
Enable/disable skills

Skill types:

Code generation skills
File operation skills
Data processing skills
Custom extension skills

3. Session Management

Functions:

Create new sessions
Manage multiple parallel sessions
Send prompts in real time
View session history

Implementation:

Each session runs independently
Supports switching between sessions
Saves session state

4. MCP Protocol Support

OpenWork natively supports Model Context Protocol:

Functions:

Connect to MCP servers
Call MCP tools
Manage MCP resources

Application scenarios:

Extend AI agent capabilities
Integrate external tools
Access external data sources

5. SSE Event Stream

Functions:

Subscribe to real-time execution events
Track agent execution progress
Receive agent responses

Implementation:

Server-Sent Events protocol
Real-time push updates
Low-latency response

Tech Stack Details

OpenWork uses a modern tech stack:

Frontend/Interface:

TypeScript (72.1%): Primary programming language
React: UI framework
JavaScript (12.9%): Auxiliary scripts

Desktop runtime:

Tauri: Cross-platform desktop framework
Rust (9.1%): Tauri core runtime

Build tools:

pnpm: Package management and workspace management
Node.js: JavaScript runtime

Why this tech stack?

Technology	Reason for choice
Tauri	Lighter than Electron, better security
TypeScript	Type-safe, great developer experience
React	Mature ecosystem, component-based development
Rust	High performance, memory safety
pnpm	Efficient package management, monorepo support

Practical Use Cases

Case 1: Local Development Environment

Scenario: Set up an AI-assisted development environment locally.

Implementation steps:

# 1. Clone and start OpenWork
git clone https://github.com/different-ai/openwork.git
cd openwork
pnpm install
pnpm dev

# 2. Create a new session in the desktop app

# 3. Install needed skills (e.g., code generation skills)

# 4. Start interacting with the AI agent

Result: A complete AI agent workbench running locally — no cloud services needed, data fully localized.

Case 2: Team Shared Server

Scenario: Set up a shared AI agent server for the team.

Implementation steps:

# 1. Start OpenWork on the server (Host Mode)
pnpm dev:ui  # or use openwrk CLI

# 2. Team members connect in Client Mode
# In OpenWork, select "Client Mode"
# Enter server address to connect

# 3. Share skills and workflow templates

# 4. Team members can collaboratively use the same AI agent

Result: Team shares AI agent resources, unified management of skills and workflows, improved collaboration efficiency.

Case 3: MCP Integration Workflow

Scenario: Extend AI agent capabilities through MCP protocol.

Implementation steps:

# 1. Start OpenWork

# 2. Configure MCP server connection
# Add MCP server address in settings

# 3. AI agent automatically gains MCP tool capabilities

# 4. Use MCP-provided tools in sessions

Result: AI agent capabilities greatly expanded — can access databases, file systems, external APIs, and more.

Case 4: Automated Workflow

Scenario: Create automated workflows for repetitive tasks.

Implementation steps:

# 1. Create a session in OpenWork

# 2. Execute a series of operations to complete the task

# 3. Save the workflow as a template

# 4. Later, execute the same workflow with one click

Result: Repetitive tasks execute automatically, saving time and effort.

Project Resources

Official Resources

🌟 GitHub: https://github.com/different-ai/openwork
📦 Releases: Download page

Related Projects

OpenCode: The core engine for OpenWork
Owpenbot: WhatsApp integration
Openwrk: CLI host mode

Who Should Use This

OpenWork is suitable for:

1. Developers

✅ Developers who need a local AI agent environment
✅ Users interested in Claude Cowork but unwilling to pay
✅ Technical users who need customized AI workflows

2. Team Users

✅ Teams that need to share AI agent resources
✅ Organizations that want unified skill and workflow management
✅ Enterprises with data privacy requirements

3. AI Enthusiasts

✅ Interested in MCP protocol
✅ Want to explore AI agent technology
✅ Enjoy trying open-source tools

4. Privacy-Sensitive Users

✅ Need fully local execution
✅ Don't want data uploaded to the cloud
✅ Need to operate in offline environments

Summary

OpenWork is an excellent open-source AI agent workbench that successfully brings the core functionality of Claude Cowork into the open-source world. Through its "local-first, cloud-ready" design philosophy, it gives users complete control over their AI workflows.

Project highlights recap: