pal-mcp-server-multi-model-orchestration

What problem does it solve?

It solves the problem of getting consistently high-quality answers when different AI models have different strengths, by orchestrating them together so you can review, plan, debug, and validate work across providers.

Core Features & Use Cases

• Multi-Model Orchestration: coordinate Gemini, OpenAI, Anthropic, Grok, Azure, Ollama, and OpenRouter models in one continuous flow for consensus-driven outcomes.
• Conversation Threading & Context Continuity: preserve a shared conversation thread so later models can build on earlier analysis.
• CLI Bridging and Isolation (clink): run specialized CLI subagents in isolated contexts for heavy tasks without contaminating the main workspace.
• Use Case: run a full code-review cycle where one model explores, others validate and secure-check, then a planner generates an implementation plan followed by pre-commit validation.

Quick Start

Ask your AI assistant to use pal with gemini-pro and o3 to review the security and logic of the authentication module, then consolidate issues and recommend the next implementation steps.