Agenthub

What Is Agenthub?

Agenthub is a multi-agent collaboration plugin designed for advanced software development workflows. It enables the parallel execution of multiple AI or human subagents on the same task, each operating in a separate, isolated git worktree. This setup allows agents to independently pursue different approaches, optimizations, or hypotheses. Upon completion, their results are evaluated—either by a custom metric or by leveraging a language model as a judge. The best-performing solution is then merged into the main branch, while other branches are archived. Agenthub is ideal for scenarios where exploring multiple solutions in parallel can drive better outcomes, such as code optimization, content generation, or research tasks. The plugin requires a git repository as its foundation and leverages git worktree features for safe concurrent development.

Why Use Agenthub?

Traditional development workflows often rely on sequential iteration or manual branching to explore different solutions to a problem. This can be time-consuming and may limit the diversity of approaches explored. Agenthub introduces a structured, automated way to foster competition and collaboration among multiple agents. The key benefits include:

• Parallel Exploration: Simultaneously investigate multiple strategies or implementations, increasing the chance of finding optimal solutions.
• Automation: Automates branch creation, agent spawning, result evaluation, and branch merging, reducing manual overhead.
• Reproducibility: Isolates each agent’s work in a dedicated git worktree, ensuring clean state management and facilitating reproducibility.
• Objective Evaluation: Supports both metric-based and LLM-based judging, ensuring that the best result is chosen according to defined criteria.
• Scalability: Easily scales up to large numbers of agents, limited only by system resources.

Agenthub is especially useful for engineering teams aiming to accelerate development cycles, maximize code quality, or foster healthy competition among AI agents or contributors.

How to Get Started

Agenthub requires an existing git repository. The installation and basic workflow are as follows:

1. Clone the Agenthub repository:

   bash

   git clone https://github.com/alirezarezvani/claude-skills.git
   cd claude-skills/engineering/agenthub

2. Ensure you have git installed and your project is under version control.

3. Initialize a collaboration session: Use the /hub:init command to start a new task. Specify the task description, number of agents, and evaluation criteria.

   bash

   /hub:init --task "Optimize the sorting algorithm" --agents 3 --eval "runtime"

4. Spawn subagents: Launch the specified number of agents, each in an isolated worktree.

   bash

   /hub:spawn

5. Monitor agent progress: Check the current state, agent progress, and branch status.

   bash

   /hub:status

6. Evaluate results: Rank the outcomes using a metric or have an LLM judge the submissions.

   bash

   /hub:eval --method "metric"

7. Merge the winning branch: Integrate the best solution into the main branch and archive the others.

   bash

   /hub:merge

8. One-shot workflow: For a streamlined process, use the /hub:run command to execute the full lifecycle in one step.

   bash

   /hub:run --task "Refactor legacy module" --agents 4 --eval "llm"

Key Features

• Multi-Agent Parallelism: Spawn N independent agents, each in its own git worktree.
• Evaluation Flexibility: Support for both quantitative metrics and qualitative LLM-based judging.
• Branch Isolation: Agents cannot interfere with each other, ensuring clean work environments.
• Agent Templates: Define reusable agent behaviors and iteration patterns for common tasks.
• Status Visualization: Inspect the directed acyclic graph (DAG) of agent contributions and outcomes.
• Integrated Message Board: Agents and users can communicate via a centralized board.
• Lifecycle Automation: Run full task cycles with a single command.

Best Practices

• Choose the Right Evaluation Metric: Tailor the evaluation method (metric or LLM) to fit the nature of your task.
• Leverage Agent Templates: Use predefined templates for common workflows, such as optimization or research exploration.
• Scale Gradually: Start with a small number of agents and increase as your system and workflow mature.
• Monitor Resource Usage: Parallel agents can be resource-intensive; ensure your system can handle the intended scale.
• Document Agent Outputs: Make use of branch naming conventions and commit messages for easy traceability.

Important Notes

• Git Dependency: Agenthub relies on git and the worktree feature; ensure your environment is properly configured.
• Conflict Resolution: Although worktrees provide isolation, be cautious of merge conflicts if agents touch overlapping code.
• LLM Evaluation Costs: Using language models for judging may incur additional usage costs and should be configured appropriately.
• Security Considerations: When running untrusted agent code, sandboxing or additional security precautions are recommended.
• Open Source Licensing: Agenthub is released under the MIT License, encouraging modification and redistribution.
• Documentation: For the latest updates, templates, and advanced configuration, refer to the official repository.