sol-luna-orchestrator

Sol-Luna Orchestrator

Overview: The Sol-Luna Orchestrator provides an adaptive orchestration framework for delegating implementation tasks to worker threads utilizing OpenAI Codex. This MCP (Multi-Channel Processing) server allows a supervising agent, referred to as Sol, to determine when delegation to specialized workers, called Luna, is advantageous. It effectively manages the delegation of tasks in isolated environments, ensuring efficient workload handling with specific file scopes, scope-violation detection, and result validation.

Key Features:

• Dynamic Task Delegation: Sol decides the necessity of delegation, optimizing the workflow by avoiding unnecessary task distribution and dynamically adjusting worker effort based on task complexity.
• Isolated Worker Threads: Each Luna worker executes tasks in its own Git worktree, minimizing conflicts and ensuring tasks are compartmentalized.
• Verification of Results: Results from Luna workers are verified by Sol, ensuring that the outcomes are accurate and reliable rather than assumed.

Example Usage

The delegate_tasks function allows for parallel and sequential handling of multiple tasks. Here’s an example of its implementation:

delegate_tasks({
  mode: "parallel",
  tasks: [
    { objective: "Implement the retry helper...", effort: "medium",
      allowedFiles: ["src/retry.mjs"], verificationCommands: ["node --test test/retry.test.mjs"] },
    { objective: "Implement money formatting...", effort: "high",
      allowedFiles: ["src/money.mjs"], verificationCommands: ["node --test test/money.test.mjs"] },
    { objective: "Diagnose the ordering bug...", effort: "xhigh",
      allowedFiles: ["src/pool.mjs"], verificationCommands: ["node --test test/pool.test.mjs"] },
  ],
})
// Result: 3/3 passed · 3 isolated worktrees · no integration conflicts · changes merged

Adaptive Strategy

The orchestration model is built on two central principles:

• Decision-Making Thresholds: Sol assesses whether delegation is warranted based on the size and complexity of the task. This prevents unnecessary coordination costs when the supervisory agent can efficiently handle smaller tasks alone.
• Effort Allocation: Each Luna worker receives a assigned effort level based on the task at hand, allowing for a tailored approach to task execution. This ensures that higher reasoning effort is allocated only to the tasks that truly require it.

Application Scenarios

• Ideal for developers using OpenAI Codex who need structured task management in moderate to large codebases.
• Particularly beneficial in situations where tasks are genuinely separable, and detailed file scopes are necessary for clarity and accuracy.
• Useful for teams experimenting with multi-agent setups that require explicit delegation boundaries and accountability in process verification.

Conclusion

The Sol-Luna Orchestrator is a valuable tool for optimizing the way tasks are handled in software development environments utilizing OpenAI Codex, delivering flexibility, efficiency, and accountability in workload management.