Scholarly orchestrates multiple AI models to produce literature reviews that are broader, more balanced, and better cited than any single model alone.
Submit one research question and watch multiple AI models work simultaneously. Results stream to your screen in real time — no waiting for sequential queries.
The comparison view automatically extracts the scholars, works, and claims each model produced — then shows you exactly where they agree and where each one stands alone.
A dedicated adjudicator model weighs the parallel outputs, resolves contradictions, and writes a single coherent review combining the strongest contributions of each.
Switch modes to trace how an idea developed over time — origins, key turning points, debates, and the current state of the art, mapped across your model panel.
Names and works mentioned by each model are normalized and deduplicated into a shared bibliography, so overlapping citations surface instantly.
Run against your full provider pool or randomly sample a subset. Fewer models for quick scans, the whole panel for deep dives.
Responses arrive token-by-token over server-sent events. Watch each model think in its own column, with per-model completion status and a stop control.
Configure any OpenAI-compatible provider — DeepSeek, OpenRouter, Groq, and more — from the settings page. Keys are stored server-side and never sent to other browsers.
Take your fused review with you. Export the finished synthesis as Markdown for your notes or as a formatted PDF for sharing.
No API keys configured? Scholarly runs in demo mode with clearly labeled sample output — never fabricated results disguised as real. Add your provider keys in Settings to go live.