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dsh-agent-arena

Workflow Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install Tikzen/dsh-agent-arena

Paste the following prompt into your AI chat to install this plugin:

In DeepSeek Harness, run the command 'dsh plugin install Tikzen/dsh-agent-arena' to install this plugin, with the full source address at https://github.com/Tikzen/dsh-agent-arena.

About this plugin

In the era of AI-augmented work, coordinating multiple AI models on complex tasks often leads to fragmented communication, redundant efforts, and lost context. dsh-agent-arena addresses these challenges by bringing humans and multiple AI entities together in a single persistent collaboration group, enabling discussions, task execution, and decision-making to flow as naturally as a team chat, thereby resolving the inefficiencies and management chaos inherent in multi-AI workflows.

The plugin's core strength lies in simulating a real-world team environment. Each AI operates with its own identity and permissions, working in parallel on tasks, utilizing tools to produce outcomes, and managing progress through task boards, decision boards, and outcome repositories. An admin intelligently filters information to keep collaboration focused, while humans retain final control. Supporting various meeting formats—from code reviews to brainstorming—it adapts to diverse use cases, making the collaborative process transparent and traceable.

Whether you're a development team, a research group, or a content creator, if you need to combine the expertise of multiple AIs to tackle complex problems, dsh-agent-arena can significantly boost your collaborative efficiency. It's ideal for users tired of juggling multiple chat interfaces and seeking seamless, intelligent teamwork. By unifying all interactions in one interface, it makes multi-AI collaboration accessible without the hassle of manual coordination.

Screenshots

Use Cases

  • In code review meetings, multiple AIs concurrently audit code and suggest improvements.
  • In research projects, AI teams collaboratively explore problems, generate solutions, and compare via decision boards.
  • During content creation, multiple AIs work together, logging outcomes to an outcome repository for human verification.

Best For

  • Software development teams needing efficient code review and collaborative debugging.
  • Researchers leveraging multi-AI capabilities to accelerate literature reviews and experiment design.
  • Enterprise collaboration teams integrating AI tools to enhance project management and decision efficiency.