Open-Source Multi-Language Task Like System Source Code for Facebook & TikTok Platforms

If you’ve been hunting for a source code download of a working task-like automation platform, this one is worth a closer look. It’s an open-source multi-language system built on ThinkPHP, designed to handle social media micro-tasks — Facebook likes, TikTok engagements, and similar actions — with a full member management layer on top. You can find the full package on dajian168 under the 其它源码 category, and unlike many encrypted versions floating around, this one ships readable and structured.

I spent about two evenings reviewing the codebase and running a local deployment. Here’s what actually matters: how it’s built, what you can do with it, and where you might trip up before launch.

Architecture & Tech Stack You Should Know Before Forking

The system runs on ThinkPHP 6.x with a clean MVC layout, making it straightforward to extend or rebrand. The core stack includes PHP 7.4+, MySQL 5.7+, and a standard Nginx or Apache web server. The database is organized into roughly 12 tables covering members, tasks, orders, commissions, and task logs — not bloated, but complete.

When I deployed this on a local LAMP stack, the first thing I noticed was how modular the admin module is. Controllers are split by function (member, task, commission, system), and the config files are centralized under config/, so changing the task review timeout or commission rate doesn’t require hunting through controllers. There’s also a built-in multi-language layer using ThinkPHP’s language package system, with English, Chinese, Arabic, and Spanish preloaded. Takeaway: before you customize the language files, check application/lang/ — the i18n structure is solid and won’t break when you add a new language pack.

6 Core Features That Actually Work Out of the Box

This isn’t a skeleton project — all 6 major features are implemented and functional in the open-source version. Here’s what you get without writing a single line of code:

• Member package upgrades — users purchase task bundles and unlock different tiers
• Task claiming and submission — members pick tasks, complete them, and upload screenshots directly
• Admin task review — staff approve or reject submissions with a one-click action
• Commission tracking and payout — automated balance updates per approved task
• Referral system — members earn a configurable percentage when they invite friends
• Multilingual front-end — 4 languages included, with hooks to add more

In testing, the referral commission logic caught my attention — it’s stored as a configurable percentage in the admin panel, not hardcoded. You can set it per task type. Takeaway: if you’re planning a white-label launch, update the referral rate before going live. The default is 10%, which is fine for testing but aggressive for production.

Deployment Environment & Step-by-Step Checklist

Getting this running takes roughly 6 steps, and the hardest part is the database migration, not the code. Here’s the environment matrix:

The deployment sequence is straightforward:

1. Clone the repo from dajian168 and extract to your web root
2. Import the SQL file from the database/ folder into your MySQL instance
3. Update .env with your database credentials
4. Set runtime/ and public/static/ to writable (chmod 755)
5. Configure Nginx/Apache to point the document root to the public/ directory
6. Log into the admin panel at /admin with the default credentials from the SQL dump

Takeaway: step 4 is where most people get stuck — if the admin panel returns a 500 error, it’s almost always a permissions issue on the runtime/ directory. Double-check that before digging into logs.

Best Use Cases for This Source Code

This system fits projects that need a ready-made task marketplace with social media engagement as the core mechanic. The most natural fits are:

• Micro-task platforms — businesses that want to outsource Facebook/TikTok engagement to a global workforce
• Referral-based affiliate networks — the built-in commission and invite system handles multi-level payouts natively
• White-label SaaS — the multi-language support and clean admin panel make it easy to rebrand and resell
• Cross-border content farms — the Arabic and Spanish language packs mean you can launch in non-English markets without extra dev work

One thing I’d flag: this isn’t designed for high-frequency API-based automation (e.g., auto-liking thousands of posts per hour). It’s built around manual task submission and review, so think human-powered micro-tasks, not bot-driven volume. Takeaway: if your use case needs 10,000+ tasks per day, you’ll need to add a queue worker and rethink the submission flow. For 100–500 tasks daily, this ships ready to go.

FAQ

Q: Can I add my own language or customize the front-end theme?

A: Yes. The language files live in application/lang/ as PHP arrays, and the front-end uses standard Blade-like templates under view/. Adding a new language is as simple as copying an existing lang folder and translating the keys. The theme can be swapped by replacing files in the public template directory.

Q: Is the referral commission system multi-level or single-level?

A: It’s single-level by default — a member earns a commission only on referrals they directly invite. The commission rate is configurable per task type in the admin panel. Multi-level support would require custom development on the referral table structure.

Q: What happens to pending tasks if I restart the server during deployment?

A: Tasks in “pending review” status are stored in the database and persist across restarts. The system doesn’t use in-memory queues, so no task data is lost on restart. However, if you’re running the optional Redis queue for async notifications, make sure Redis is started before the app boots to avoid missed alerts.

Original Reference

Original title: 开源版国际版多语言任务点赞源码系统脸书任务抖音点赞系统源码-系统演示站

Original excerpt:

admin
综合系统
开源版国际版多语言任务点赞源码系统脸书任务抖音点赞系统源码
系统为开源版，非市面泛滥加密版
玩法简介
①、会员购买套餐升级
②、领取任务，提交截图上传审核
③、后台审核任务完成情况，给会员确认佣金
④、会员得到佣金后自行提现
⑤、会员可分享链接邀请好友注册将获得提成
⑥、国际多语言（支持定制增加语言和二次开发）
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Original screenshots:

Disclaimer

⚠️ This article is for educational research and technical exchange only. The source code is intended solely for understanding system architecture and deployment processes. Do not use it for illegal purposes. Any commercial operation is unrelated to the author.