Predefined Model & Agent Presets in Paddler Dashboard

[DB379]

It would be great to have a dashboard feature where users can define preconfigured models with a fixed number of agents (and capacity). This would allow users to set up their preferred models once and avoid repeatedly configuring the same setup every time they want to run experiments or deploy agents.

Instead of manually choosing a model and adjusting agents each time, users could:

• Predefine a model configuration (e.g., select a model and specify the number of agents and slots per agent)
• Save it as a preset for future use
• Enable, edit, delete, or disable presets directly from the dashboard

Presets would allow users to quickly apply commonly used configurations without extra steps, providing a more streamlined and out-of-the-box experience. Users could maintain a library of presets for different purposes, such as small tests, medium-scale runs, or full-scale deployments, and reuse them whenever needed.

Practical Examples of Presets

• Small Chat Model – 2 agents × 4 slots each
• Medium Embedding Model – 3 agents × 6 slots each
• Large Code Model – 5 agents × 8 slots each
• Experimental Model – 1 agent × 2 slots, for testing purposes

Additional benefits include:

• Consistency: Ensures the same model and agent setup is used every time, reducing errors and misconfigurations
• Reproducibility: Makes it easier to reproduce experiments or results by applying the same preset setup
• Scalability: Simplifies scaling, since users can adjust agent counts in presets and apply changes with minimal effort
• Time efficiency: Saves time for users by automating repetitive configuration tasks

In short:
Users can choose a model once, define how many agents it should run with, save it as a preset, and manage these presets from the dashboard—enabling quick deployment, editing, scaling, and testing without repeated setup.