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Danghuangshang

Danghuangshang is an open-source TypeScript framework for deploying and coordinating specialized autonomous AI agents. Its project metadata connects it with OpenClaw and Discord.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Danghuangshang — OpenViST project profile in Multi-Agent Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a project requires several specialized agents to collaborate or when you want to explore an open-source orchestration approach rather than build around a single agent.

Who it is for

  • Developers exploring multi-agent AI systems
  • Teams designing role-based agent workflows
  • Builders working with TypeScript, OpenClaw, or Discord-related projects

Documented uses

  • Coordinate specialized AI agents around distinct roles.
  • Assign work across a collaborative group of agents.
  • Develop a Discord-oriented multi-agent bot workflow.
  • Experiment with an orchestration layer for AI roles.
Before you choose

Requirements & limits

The repository is public, open source, MIT-licensed, and not archived. The supplied metadata identifies Cloud, self-hosted, and server contexts.

  • Whether your project needs multiple specialized agents rather than one general-purpose agent.
  • Compatibility with a TypeScript development workflow.
  • Relevance of OpenClaw or Discord to your intended deployment.
  • Whether the MIT-licensed open-source repository fits your adaptation requirements.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install and configure the OpenClaw multi-agent system for Discord

Install the repository locally, choose an organizational regime during setup, and provide the LLM API key and Discord Bot Token for startup.

Before you start

  • A server is recommended by the project for new users.
  • An LLM API key.
  • A Discord Bot Token.
  • A shell environment able to run the documented installation commands.

Steps

  1. Clone the repository and enter its directory: the command below followed by cd danghuangshang.

    Documented command
    git clone https://github.com/wanikua/danghuangshang.git

  2. Run the full installer with the command below.

    Documented command
    bash scripts/full-install.sh

  3. Choose one of the documented regimes during installation: Ming dynasty cabinet, Tang dynasty three ministries, or modern enterprise.

  4. When prompted, enter the LLM API key and Discord Bot Token.

  5. Use the documented Discord configuration so allowBots is set to mentions, preventing bots from responding to other bots unless mentioned.

Expected outcome

The documented installation and configuration flow sets up the multi-agent system and its Discord bot configuration; the README then describes addressing a bot in Discord with an @mention.

Guide limitations

  • The documentation recommends a cloud server for beginners rather than a personal computer.
  • The evidence does not document provider-specific API-key steps, Discord application creation, or a verification procedure.
  • The documentation warns that setting allowBots to true can cause bot-triggered message loops.
Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Agent orchestration

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
DanghuangshangCoordinate specialized AI agents around distinct roles.Cloud, Self-hosted, ServerThe repository is public, open source, MIT-licensed, and not archived. The supplied metadata identifies Cloud, self-hosted, and server contexts.
MassgenRun multiple AI agents against the same user task.CLIThe repository documents installation through pip or uv and requires Python 3.11 or later. Setup can configure API keys for OpenAI, Anthropic, Google and xAI; credentials are needed for the selected model backends.
Langchain Multi Agents BoilerplateRoute incoming football questions to model and agent workflows according to task complexity.WebThe repository is public and specifies an MIT license. Running the application requires OpenAI and Groq API keys and a Redis server.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
wanikua/danghuangshang · Official homepage title · retained in its source language
How we review sourcesSuggest a correction