multi-llm-orchestration

You can orchestrate a multi-LLM review by dispatching parallel review requests to configured LLM clients. Use the command `multi-llm-orchestration --review "Your prompt" --models codex,gemini,grok` to coordinate simultaneous analysis across models.

Cache-aware prompting utilizes structured `promptParts` to maximize cache efficiency during LLM orchestration. This mechanism optimizes performance by reducing redundant processing when coordinating parallel dispatches across diverse models like Claude and Mistral.

Yes, configuring MCP (Machine Control Plane) servers is required for multi-LLM orchestration. You must set up multiple LLM clients and connect them to MCP servers to handle parallel dispatches, session continuity, and cross-LLM workflows effectively.

Yes, multi-LLM orchestration explicitly handles session continuity when delegating tasks across Claude, Codex, and Mistral. The workflow manages state and context across parallel dispatches to ensure seamless collaborative reviews without manual intervention.

The best way to handle parallel dispatches is through a multi-LLM orchestration workflow that coordinates multiple LLM clients simultaneously. This approach manages parallel task delegation and structured prompt caching for comprehensive cross-LLM analysis.

The primary limitation is the prerequisite setup; coordinating diverse models like Grok and Gemini requires pre-configuring individual LLM clients and MCP servers. Without proper MCP integration, parallel dispatches and session continuity across models cannot function.