One Open Source Project a Day (No. 63): AiToEarn - An All-in-One AI Platform for Turning Content into Revenue

Introduction

"Let's use AI to Earn!"

This is the No.63 article in the "One Open Source Project a Day" series. Today, we are exploring AiToEarn.

This project has a very different flavor from the ones we have covered recently. The last few articles were all about infrastructure layers: agent frameworks, engineering workflows, GUI control engines. AiToEarn goes straight for the one-person company's ultimate question: how do you use AI to turn content into money?

Its premise is refreshingly direct: a content creator's biggest pain is not "knowing how to create"—it is the fact that publishing to 12 different platforms means 12 separate manual uploads, responding to comments means hours of repetitive typing, and landing brand deals means endless negotiation. AiToEarn uses four AI agents to automate all of this: Create → Publish → Engage → Monetize, forming a complete content monetization loop.

10.9k Stars, 2,591 commits, 26 releases—this is not a demo project. It is a production-ready content monetization tool.

What You Will Learn

• How each of AiToEarn's four AI agent capabilities (Create / Publish / Engage / Monetize) works
• How "one-click publishing" to 12+ platforms—including TikTok, YouTube, Douyin, and Xiaohongshu—is achieved
• The design rationale behind three monetization models: CPS, CPE, and CPM
• How MCP protocol integration lets Claude and Cursor directly schedule content publishing tasks
• The right deployment method for your use case (Web / OpenClaw / MCP / Docker / local dev)

Prerequisites

• Basic familiarity with content creation workflows
• Node.js environment (for local deployment)
• No specific programming language background required

Project Background

Project Introduction

AiToEarn is an open-source AI content marketing platform. Its core mission is to help individual creators, small teams, and enterprises automate content production, cross-platform distribution, and commercial monetization across 12+ global content platforms.

The target user profile is clear: the solo-founder content creator—someone simultaneously running accounts on Douyin, Xiaohongshu, Bilibili, TikTok, and YouTube, who needs to produce content daily, publish to each platform individually, respond to comments, and negotiate brand deals. There is never enough time. AiToEarn's vision is to have AI agents handle all the repetitive work, leaving creators free to focus on the genuinely valuable creative parts.

Author/Team Introduction

• Developer: yikart team
• Positioning: An all-in-one solution for Chinese creators going global and for content monetization both domestically and internationally
• Development timeline:
  • Sep 2025: International version launched, supporting Western platforms (TikTok, YouTube, Instagram, etc.)
  • Dec 2025: AI agent auto-generation and publishing capabilities launched
  • Mar 2026: Content marketplace launched, MCP protocol support added
  • Apr 2026: OpenClaw monetization support added

Project Data

• ⭐ GitHub Stars: 10,900+
• 🍴 Forks: 2,000+
• 👀 Watchers: 383
• 📦 Latest Version: v2.1.0 (March 2026)
• 📝 Commits: 2,591 (actively maintained)
• 📄 License: MIT
• 🌐 Repository: yikart/AiToEarn
• 🌏 Website: aitoearn.ai (international) / aitoearn.cn (China)

Main Features

Core Utility

AiToEarn abstracts the complete content creator workflow into four AI agent capabilities, forming an automated pipeline from "idea" to "income":

Creative idea / Brand task
        ↓
  🎨 Create  (AI generates video/image/text content)
        ↓
  📢 Publish (One-click distribution to 12+ platforms)
        ↓
  💬 Engage  (Automated interactions to boost reach)
        ↓
  💰 Monetize (Complete tasks, receive payment)

Use Cases

1. Individual Creator Multi-Platform Management

   • One piece of content, automatically adapted for Douyin's vertical video, Bilibili's horizontal format, and Xiaohongshu's image-text layout—published simultaneously without manual reformatting.

2. Brand Content Marketing Automation

   • Brands post promotion tasks on the platform; creators accept and complete them; settlements happen automatically via CPS/CPE/CPM, forming a decentralized KOL network.

3. Cross-Border Content Operations

   • Chinese content is automatically translated and localized; a single workflow publishes to domestic (Douyin/Xiaohongshu/Bilibili) and international (TikTok/YouTube/Instagram) platforms simultaneously.

4. AI Tool Ecosystem Integration (MCP)

   • Via MCP protocol, trigger content publishing tasks directly from Claude or Cursor: "Post this article to my Xiaohongshu account."

5. Data-Driven Content Strategy

   • The browser extension monitors brand-related discussions across platforms, identifies high-conversion signals, and automatically adjusts engagement strategies.

Quick Start

Method 1: Web Version (Zero Config)

Visit https://aitoearn.ai → Create an account
→ Connect your social media accounts
→ Start publishing content or accepting brand tasks

Method 2: MCP Protocol (Use inside Claude / Cursor)

// Add to your MCP config in Claude or Cursor:
{
  "mcpServers": {
    "aitoearn": {
      "url": "https://aitoearn.ai/api/unified/mcp",
      "apiKey": "your-api-key-here"
    }
  }
}

Then in conversation:
"Post the article below to my Douyin and TikTok accounts"
"Show me today's engagement stats across all platforms"
"Accept this brand's promotion task"

Method 3: OpenClaw CLI

# Install
npx @openclaw/cli install aitoearn

# Use
openclaw aitoearn publish --content "Your content" --platforms "douyin,tiktok"

Method 4: Docker Deployment

# Clone the repository
git clone https://github.com/yikart/AiToEarn.git
cd AiToEarn

# Three commands to launch (Docker handles MongoDB/Redis automatically)
cp .env.example .env
# Edit .env with your configuration
docker-compose up -d

# Access at http://localhost:3000

Method 5: Local Development Mode

# Requirements: Node.js 20.18.x + pnpm
git clone https://github.com/yikart/AiToEarn.git
cd AiToEarn
pnpm install

# Start backend
pnpm run dev:backend

# Start frontend
pnpm run dev:frontend

Core Characteristics (Four Agent Capabilities)

1. 🎨 Create (AI Content Creation Agent)

This agent handles the complete "concept to finished product" content pipeline:

Input: Brand task description / creative keywords / reference examples
  ↓
AI Agent processing:
  - Script generation (based on task requirements)
  - Video generation (Grok, Veo, and other video models)
  - Image + text generation (text-to-image + layout)
  - Translation and localization (bilingual adaptation)
  - Batch content variant generation
  ↓
Output: Platform-adapted finished content

Supported generation capabilities:

• Video: AI video generation (Grok/Veo integration), editing, subtitle generation
• Image + text: AI image generation, multi-image layouts, copywriting optimization
• Batch production: Generate multiple differentiated versions from one theme to prevent account similarity flags

2. 📢 Publish (Multi-Platform Publishing Agent)

Supported platforms (12+):

Core publishing capabilities:

• Calendar scheduling: Unified management of publish times across all platforms
• Format auto-adaptation: Horizontal/vertical switching, title length adaptation to each platform's specs
• Account matrix management: Publish the same content to multiple accounts on the same platform
• Publish status tracking: Real-time view of results on each platform

3. 💬 Engage (Interaction Agent Browser Extension)

The engagement agent runs as a browser extension, providing:

• Smart comment replies: AI generates context-appropriate responses—not mechanical templates
• Automated likes/follows: Interact with target user groups according to strategy
• Brand monitoring: Capture brand-related discussions across platforms in real time
• High-conversion signal detection: Identify comments showing purchase intent for priority engagement

4. 💰 Monetize (Monetization Agent)

The most distinctive part of AiToEarn—a decentralized content monetization marketplace:

• Brands: Post promotion tasks (specify platform, content requirements, budget)
• Creators: Browse and accept tasks that match their style
• AI Agent: Assists with content creation, publishing, and data tracking
• Settlement system: Automatically settles via three payment models

Three monetization models:

Project Advantages

Detailed Analysis

1. Technical Architecture: TypeScript Monorepo

AiToEarn's technology choices are pragmatic:

AiToEarn (Nx Monorepo + pnpm)
├── apps/
│   ├── web/           ← Next.js frontend (user interface)
│   ├── api/           ← NestJS backend (business logic + task scheduling)
│   ├── desktop/       ← Electron desktop client
│   └── browser-ext/   ← Browser extension (Engage agent)
├── packages/
│   ├── ai-core/       ← AI model call layer (video/image generation)
│   ├── publisher/     ← Multi-platform publishing engine (core)
│   ├── scheduler/     ← Content scheduling management
│   └── mcp-server/    ← MCP protocol server
└── docker-compose.yml ← One-command deployment config

Key technology choices:

• TypeScript (92.6%): Full-stack type safety with shared type definitions across frontend and backend
• Nx Monorepo: Unified management of multiple sub-projects with shared infrastructure
• NestJS: Backend framework—modular architecture suits complex business logic
• Electron: Desktop client that bypasses browser login restrictions for platform authorization

2. The Multi-Platform Publishing Engine: The Core Technical Challenge

"One-click publishing to 12+ platforms" sounds simple but is technically the most demanding part of the project. Each platform presents unique challenges:

• Session management: Each platform's Cookie/Token has different expiry patterns—automatic refresh required
• API heterogeneity: Some platforms have official APIs (YouTube), others only support browser simulation (Xiaohongshu)
• Content spec differences: Video duration limits, cover image ratios, and hashtag formats all vary per platform
• Anti-automation detection: Platforms detect and throttle automated actions—real user behavior simulation is essential

AiToEarn's approach:

Unified publishing interface
        ↓
Platform adapter layer (one Adapter per platform)
        ↓
Playwright-driven browser automation (for platforms without APIs)
  +
Official API calls (for platforms with open APIs: YouTube, LinkedIn, etc.)

3. MCP Integration: Let AI Tools Schedule Content Publishing

The MCP support added in March 2026 is AiToEarn's most forward-looking feature. It exposes AiToEarn's capabilities as MCP tools, allowing Claude, Cursor, and any MCP-compatible AI assistant to directly schedule content publishing:

User says to Claude:
"Take the article I just wrote, generate a cover image,
and schedule it to publish on Xiaohongshu and LinkedIn
tomorrow at 9am"

Claude calls AiToEarn via MCP:
  → aitoearn.generate_cover(article_content)  # Generate cover image
  → aitoearn.schedule_post({                  # Schedule the post
      content: article_content,
      cover: generated_cover,
      platforms: ["xiaohongshu", "linkedin"],
      scheduled_at: "2026-05-13T09:00:00+08:00"
    })
  → Returns: "Scheduled. Content will publish to 2 platforms tomorrow at 9:00am"

This design transforms AiToEarn from a standalone tool into a node in the broader AI workflow ecosystem.

4. The Monetization Marketplace: A Decentralization Experiment in the Creator Economy

AiToEarn's content marketplace is an interesting experiment: decentralizing the traditional MCN model of "brand meets creator."

Traditional flow:

Brand → Contact MCN → MCN screens KOLs → Negotiate pricing
     → Creator accepts → MCN takes 30–50% commission

AiToEarn flow:

Brand → Post task (platform/requirements/budget) → Creator browses and accepts
     → AI assists with creation → Smart settlement
                                        ↑
                        AiToEarn platform takes a lower service fee

The value proposition: lower the barrier for brand marketing (small and mid-size brands can now do KOL campaigns) while increasing creator earnings (fewer middlemen).

Project Links & Resources

Official Resources

• 🌟 GitHub: https://github.com/yikart/AiToEarn
• 🌏 International: https://aitoearn.ai
• 🇨🇳 China: https://aitoearn.cn
• 🔌 MCP Endpoint: https://aitoearn.ai/api/unified/mcp

Target Audience

• Solo-founder content creators: Running multi-platform accounts who need to automate repetitive operations
• Brand marketing teams: Looking for a decentralized, low-barrier KOL collaboration channel
• Cross-border content operators: Creators and teams needing to cover both domestic and international platforms
• AI tool developers: Embedding content publishing capabilities into their AI workflows via MCP

Summary

Key Takeaways

1. Four AI agents (Create / Publish / Engage / Monetize) covering the complete content monetization lifecycle
2. 12+ platform support: domestic and international mainstream platforms managed under a single workflow
3. MCP integration makes AiToEarn schedulable directly from Claude, Cursor, and other AI tools
4. Decentralized monetization marketplace (CPS/CPE/CPM) lowers the barrier for brand marketing while increasing creator earnings
5. TypeScript Monorepo architecture, actively maintained (2,591 commits, 26 releases)

One-Line Review

AiToEarn hands off every repetitive task in "content entrepreneurship" to AI—if you are a solo-founder content creator, it may be the most direct tool for actually making "use AI to earn" a reality.

Find more useful knowledge and interesting products on my Homepage