838997125

838997125 / Tang-Political-System

Public

基于三省六部架构的 AI 多 Agent 协作系统 · Windows 优化版

github.com838997125Tang-Political-System css docker html muilt-agents openclaw
10
1
100% credibility
Found Mar 16, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A real-time dashboard for orchestrating AI agents structured as China's ancient Three Departments and Six Ministries bureaucracy, with task tracking, model switching, and skill management.

How It Works

1
👑 Discover the Imperial AI Court

You find a clever system that turns AI helpers into an ancient Chinese government, where smart agents work together like ministers in a royal court.

2
🚀 Set Up with One Click

Download and run the easy installer that prepares all your AI ministers and their workspaces automatically.

3
📊 Open Your Royal Dashboard

Launch the beautiful control room where you see live task boards, agent statuses, and everything at a glance.

4
📜 Issue Commands to Ministers

Send tasks or edicts through chat, and watch the planning, review, and execution flow through departments.

5
🔍 Monitor and Intervene Live

Keep an eye on progress across panels for tasks, officials, models, skills, and daily briefings, pausing or adjusting as needed.

6
🧠 Tune Ministers' Abilities

Swap their thinking models or add new skills right from the dashboard to make them smarter for your needs.

✅ Celebrate Completed Work

Enjoy reliable results from your AI court, with full archives, stats, and ceremonies marking every success.

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AI-Generated Review

What is Tang-Political-System?

This Python project deploys a multi-agent AI system modeled on the Tang Dynasty political system, with three departments for planning, review, and dispatch, plus six ministries for specialized execution. Users get a real-time dashboard at localhost:7891 showing task kanban, agent health, model switching, and news feeds—issue commands via chat, and agents triage, audit, and collaborate with strict permissions. Docker one-click demo or install.sh handles Windows 11 agent setups, pulling in OpenClaw for agent github claude, copilot, or openai backends.

Why is it gaining traction?

Unlike CrewAI or AutoGen's flat agent chats, it enforces institutional gates—planning proposals get mandatory review vetoes before execution—yielding auditable outputs on complex tasks like code reviews or reports. The immersive dashboard tracks heartbeats, token costs, and interventions like task stops or model swaps per agent, plus Tang Dynasty theming that sticks. Windows exe/service optimizations make agent github action workflows dead simple, no devops hassle.

Who should use this?

AI engineers orchestrating agent github copilot reddit-style teams for code gen, compliance checks, or data analysis, especially on Windows proxmox/zabbix/veeam setups needing veeam agent windows update flows. Teams tired of opaque multi-agent runs wanting a microsoft/openai agent github repo with built-in QA, like planning API designs or competitor analysis via agent github code pipelines.

Verdict

Promising for structured agent windows download scenarios, but at 10 stars and 1.0% credibility, it's early—docs shine with demos, but test coverage lags for prod. Try the Docker image first if Tang Dynasty political system appeals for reliable AI bureaucracy.

(198 words)

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