Multi-model interfaces are finally breaking out of the lab. The latest UIs let you compare, ensemble, and explore outputs from several models in one place. A2UI from Google pushes a protocol-agnostic path for LLM-generated UIs, using streamable JSON Lines and a framework-agnostic format [1].
Show HN UI means non-linear exploration is becoming real. This graph-based prototype addresses the limits of a linear chat by letting you connect ideas across moments in a conversation [2].
LLM Onestop unlocks a single interface for GPT-4, Claude, Gemini, and Llama—with side-by-side comparisons and a free tier, plus a 'Connect' plan to bring your own API keys [3].
Deepvote applies an ensemble twist: 10 AI models vote on your decisions, helping you surface reasoning before you decide [4].
For production workflows, discussions point to platforms like Maxim, Langfuse, Arize, Braintrust, and Comet Opik for evaluation and monitoring, each with strengths in tracing, drift detection, or quick iteration [5].
These multi-model UIs are shaping how teams test, compare, and deploy LLMs—watch this space for graph-based exploration and smarter voting in prompts.
References
A2UI: LLM-generated UI protocol (Google)
Open-source toolkit for LLM-generated UIs; A2UI uses A2A protocol, streams JSONL, and remains framework-agnostic for native rendering.
View sourceShow HN: UI for non-linear conversations with LLMs [video]
Prototype for graph-based LLM UI; aims to improve exploration beyond linear chat, connect concepts, compare ideas
View sourceShow HN: LLM Onestop – Access ChatGPT, Claude, Gemini, and more in one interface
Unifies multiple models in one UI; compare outputs side-by-side; free tier; seeks feedback; privacy concerns
View sourceShow HN: Deepvote – 10 AI models vote on your decisions
Show HN: Deepvote uses ten AI models to vote on user decisions, enabling ensemble opinions for guidance and insights today.
View sourceCompared 5 AI eval platforms for production agents - breakdown of what each does well
Compared five eval platforms for production LLM workflows, detailing strengths in agent evaluation, rapid prototyping, production monitoring, and open-source control.
View source