A new open-source platform born from a hackathon is proving something Microsoft’s research just confirmed: a panel of specialized AIs debating a problem dramatically outperforms a single model—or a single human. The project, called Consilium, is a Gradio-based visual interface and MCP server that seats different LLMs around a virtual poker table to hash out answers through structured discussion.
The timing is uncanny. The developer, writing under the Inblix byline, had initially planned a simple MCP server for RevenueCat but pivoted to a multi-model debate system. Shortly after, Microsoft published its AI Diagnostic Orchestrator (MAI-DxO), a medical panel where agents like a “Dr. Challenger” produced a correct diagnosis rate of 85.5% on benchmark cases. Solo physicians? Just 20%. That validation is the core thesis of Consilium.
Getting models to actually debate rather than just monologue required a personality injection. The developer found that without clear roles, the LLMs didn’t know how to engage. The fix was a set of distinct personas: a “passionate expert advocate,” a “rigorous critic” tasked with finding flaws, a “strategic advisor” focused on practical implementation, and others. These roles create the productive friction needed for consensus, majority voting, or ranked-choice decision modes.
What makes the project stand out is its custom Gradio component—a poker-style roundtable where avatars display speech bubbles showing thinking status and research activity in real-time. The state management is surprisingly straightforward under the hood, using session-based dictionaries and direct JSON mutations synchronized between the Python backend and frontend rendering. The component proved so robust that after the initial build, only minor features like user-defined avatars were added. The developer even contributed back to Gradio’s documentation after hitting a snag with PyPI publishing, making this project a genuine two-track effort in both custom component design and MCP server architecture.
