🎼 [Feature Proposal] Roo Code AI Symphony — API Configuration & Adaptive Multi-Agent Orchestration

[AhmedProab]

🧠 Overview

This proposal introduces a powerful AI Orchestration Engine called Roo Code AI Symphony, inspired by production-scale multi-model architectures. It integrates API Configuration Profiles, Adaptive Task Routing, and Multi-Agent Coordination—all tailored for Roo Code’s evolving agent system.

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🎯 Key Features Proposed

1. 🔌 API Configuration per Mode

Allow each Mode (e.g., Debug, Audit, Data Analysis) to be paired with a dedicated API configuration. This enables:

• Different models per mode (Claude, DeepSeek, Gemini, etc.)
• Cost optimization (e.g., use faster models in dev, accurate ones in prod)
• Auto-routing based on complexity, task type, or security context

2. 🧩 Modular Multi-Agent Architecture

Based on customModes, agents can:

• Collaborate dynamically (example: Quantum Developer + Guardian Architect)
• Trigger fallback agents when errors occur
• Monitor CPU/memory & cost per model (via telemetry)

3. 🛡️ Security Profiles with A.R.M.O.R

• Dynamic risk assessment before auto-approval
• Enforce validation policies per Mode (e.g., review before write)

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📌 Why This Matters

Relying on a single, monolithic AI model limits both efficiency and quality — not every model excels at every task. By enabling per-mode API configurations, we unlock the full potential of multi-model orchestration:

• Use specialized models where they perform best (e.g., Claude for review, DeepSeek for coding, Gemini for design).
• Reduce cost by assigning lightweight models to low-complexity tasks.
• Improve code quality, accuracy, and strategic reasoning through adaptive model switching.
• Increase fault-tolerance with fallback agents.

In diversity, there is strength — and with orchestration, there's harmony in that diversity.

🔗 Learn more about Custom Modes in Roo Code

🚀 Impact

With this system, developers and teams can:

• Assign the right model to the right job (e.g., Claude for review, DeepSeek for coding)
• Save compute costs by routing lightweight tasks to faster/cheaper models
• Achieve 95–98% automation coverage with intelligent fallback + retry logic

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🤝 Collaboration Invitation

I'm happy to share the full orchestration config in JSON or contribute a working prototype. Let’s make Roo Code even more powerful for teams and AI builders alike.

🔗 AI Symphony Sample Config (JSON) (optional)

💡 Examples

Example: maestro-conductor mode

{
  "slug": "maestro-conductor",
  "apiConfig": "Roo Code AI Symphony",
  "autoApprove": "intelligent",
  "models": ["DeepSeek-R1-0528", "Claude Sonnet 4", "Gemini 2.5 Pro"],
  "customInstructions": "Strategic routing + performance dashboard + fallback models"
}

[AhmedProab]

👋 Hello Roo Code team,

I'm excited to share with you the AI Symphony V3.0 – MAESTRO Edition configuration, a modular and adaptive multi-agent setup I've been refining to explore the full potential of Roo Code's orchestration engine.

🔧 Currently, I’m actively working on Version 4.0, which is under development, and simultaneously architecting the roadmap for Version 5.0, with focus on:

• Collaborative memory evolution
• Predictive task routing
• Hybrid reinforcement learning

I’m sharing this configuration with the community not only as a contribution, but as a token of appreciation — a small way to give back to the amazing Roo Code team and ecosystem that continues to inspire innovation.

Let's collaborate and build together.
Wishing you all continued success and breakthroughs ahead. 🙌

– Ahmed Abdullah (aka. AhmedProab) 🥰

Multi-agent mode #279