MultiChat — Multi-Model LLM Debate & Consensus Engine (OpenAI + Claude + Gemini)

Three frontier models answer the same question in parallel, then critique each other and converge on a stronger answer.

The idea

Single-model answers are confidently wrong in ways that are hard to detect from the outside. MultiChat turns that into a measurable process: OpenAI, Claude and Gemini each produce an independent answer, then each model reviews the others, surfaces disagreements, and the system drives the exchange toward a consensus response with the disagreements made explicit.

What I built

• Parallel fan-out to three providers with normalised request/response handling, streaming, timeouts and per-provider error isolation, so one slow or failing vendor does not take the session down.
• A debate loop: answer -> cross-critique -> revision -> consensus, with the full transcript retained so a user can see why the final answer changed.
• End-to-end test coverage with Playwright against the real UI, not just unit tests on the orchestration layer.

Why it is interesting

It is a practical, cheap form of ensembling and self-critique for LLM output evaluation — useful when you need higher-confidence answers and an auditable reasoning trail rather than the fastest possible token.

Stack

OpenAI, Anthropic Claude and Google Gemini APIs, multi-agent orchestration, streaming, Playwright E2E tests, Python/FastAPI backend.

Live demo, GitHub source and architecture notes: https://www.twine.net/signin